Based on prior knowledge, we can analyze the relationship between the feature "GEPAR_S_n" and the task of determining whether the patient has chronic heart failure.

Since "GEPAR_S_n" represents the use of anticoagulants in the ICU, it is reasonable to assume that patients who have utilized anticoagulants (GEPAR_S_n = 'yes') may have a lower likelihood of developing chronic heart failure. On the other hand, patients who have not used anticoagulants (GEPAR_S_n = 'no') might have a higher probability of experiencing chronic heart failure.

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

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

In this case, since the feature "GEPAR_S_n" is a categorical variable with only two possible values ('yes' or 'no'), there is no uncertainty and no hard to predict values. Therefore, we include both possible values in the corresponding lists for each target class.