Based on prior knowledge, we can analyze the relationship between the feature "nr_08" (Ventricular paroxysmal tachycardia in the anamnesis) and the task of determining if the patient shows chronic heart failure.

To analyze this relationship, we can divide the data into two groups based on the target variable (whether the patient has chronic heart failure or not). We'll examine the distribution of "nr_08" values within each group.

Let's assume the target variable is denoted as "CHF" (Chronic Heart Failure) with categories "no" and "yes".

Here is an example analysis:

For the class "no" (patients without chronic heart failure):
- "nr_08" values: ['no', 'no', 'no', 'no', 'no']

For the class "yes" (patients with chronic heart failure):
- "nr_08" values: ['yes', 'no', 'yes', 'yes', 'no']

Based on this analysis, we can create the dictionary:

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

The list of values for the target class "no" is ["no"], and for the target class "yes" is ["yes", "no"].

Note that if there are more possible values for the "nr_08" feature, they should be included in the corresponding lists for each target class.