Based on prior knowledge and the given feature description, we can analyze the relationship between the feature "GEPAR_S_n" and the target variable - chronic heart failure.

Since the feature "GEPAR_S_n" represents the use of anticoagulants in the ICU, we can make an assumption that patients who have used anticoagulants may have a higher likelihood of experiencing chronic heart failure. Therefore, for the task of determining whether the myocardial infarction complications data of a patient show chronic heart failure, we can expect that patients who haven't used anticoagulants (category: 'no') are less likely to have chronic heart failure, while those who have used anticoagulants (category: 'yes') may have a higher chance of having 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" has only two categories - 'yes' and 'no', and we can make assumptions about the relationship between the categories and the target variable, we include both categories in the dictionary. The values for each target class are presented as strings in lists.