------
## 最终结论
Here's the complete Python code to solve the problem:

```python
def find_pile(n, piles, m, juicy_worms):
    # Calculate the cumulative sum of worms in each pile to determine the range of labels for each pile
    cumulative_worms = [0] * n
    cumulative_worms[0] = piles[0]
    for i in range(1, n):
        cumulative_worms[i] = cumulative_worms[i - 1] + piles[i]

    # For each juicy worm label, find which pile it belongs to
    result = []
    for worm_label in juicy_worms:
        # Binary search to find the correct pile
        low, high = 0, n - 1
        while low <= high:
            mid = (low + high) // 2
            if worm_label <= cumulative_worms[mid]:
                high = mid - 1
            else:
                low = mid + 1
        result.append(low + 1)  # Pile numbers are 1-indexed

    return result

# Read input values
n = int(input().strip())
piles = list(map(int, input().strip().split()))
m = int(input().strip())
juicy_worms = list(map(int, input().strip().split()))

# Find and print the pile number for each juicy worm label
results = find_pile(n, piles, m, juicy_worms)
for res in results:
    print(res)
```

### Explanation:

1. **Cumulative Sum Calculation**: 
   - We first calculate a cumulative sum array `cumulative_worms` where each element at index `i` represents the total number of worms from the start up to and including pile `i`.
   - This helps us quickly determine the range of labels that belong to each pile.

2. **Binary Search**:
   - For each juicy worm label given by Marmot, we perform a binary search on the `cumulative_worms` array.
   - The goal is to find the smallest index `mid` such that `worm_label <= cumulative_worms[mid]`. This index corresponds to the pile containing that worm.
   - We adjust indices using binary search (`low`, `high`, and `mid`) to efficiently pinpoint which pile a given worm belongs to.

3. **Output**:
   - For each query about a juicy worm's label, we output which pile it belongs to. The output is adjusted by adding 1 because piles are numbered starting from 1.

This approach ensures that even with large inputs (up to 100,000 piles or queries), our solution remains efficient due to its logarithmic search complexity combined with linear preprocessing.