Based on prior knowledge, we can analyze the relationship between the feature "poutcome" and the task of whether the client subscribes to a term deposit.

The feature "poutcome" represents the outcome of the previous marketing campaign. It is a categorical variable with four possible categories: "unknown", "failure", "other", and "success".

To analyze the relationship, we can look at the distribution of the "poutcome" categories among the two target classes (subscribe or not subscribe to a term deposit). Specifically, we want to identify which "poutcome" categories are more likely to lead to a subscription and which ones are more likely to lead to no subscription.

Based on this analysis, we can create the following dictionary:

```json
{
    "no": ["unknown", "failure", "other"],
    "yes": ["success"]
}
```

In this dictionary, we include the possible values of the "poutcome" feature for each target class. We only include the values that show a clear relationship with the target. In this case, the "poutcome" category "success" is more likely to lead to a subscription, while the categories "unknown", "failure", and "other" are more likely to lead to no subscription.

Note: If the analysis reveals that there is no clear relationship between the "poutcome" feature and the target, the list for each target class should still not be empty. In such cases, where there is no clear relationship, it is not necessary to include the hard-to-predict values in the dictionary, as mentioned in the initial question.