Based on prior knowledge, let's analyze the relationship between the feature "NITR_S" and the task of determining whether the patient shows chronic heart failure (CHF).

To analyze this relationship, we need to look at the distribution of the "NITR_S" values for each target class (yes or no for chronic heart failure). We can then determine if there is any correlation or pattern between the feature and the target.

Here is a step-by-step analysis:

1. Obtain the dataset containing the "NITR_S" feature and the target variable (chronic heart failure).
2. Group the data by the target variable (yes or no for chronic heart failure).
3. For each target class, retrieve the unique values of the "NITR_S" feature.
4. Create a dictionary in the required format, with the target class as the key and the list of unique "NITR_S" values for that class as the value.

Now, let's generate the dictionary based on the analysis:

```json
{
	"no": ["no", "yes"],  
	"yes": ["no", "yes"]
}
```

Note: Since the feature is a categorical variable with only two categories ("no" and "yes"), the possible values for each target class are the same.