Based on prior knowledge, the feature nr_11, which represents the observing of arrhythmia in the patient's medical history, could be related to the risk of chronic heart failure. Arrhythmia can be a potential risk factor for chronic heart failure, as it indicates abnormal heart rhythms and can lead to compromised cardiac function.

To analyze the relationship between feature nr_11 and the presence of chronic heart failure, we need to examine the frequency distribution of nr_11 values for each target class (yes and no) in the myocardial infarction complications data.

Now, let's generate the requested dictionary:

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

Since the feature nr_11 is a categorical variable with two categories ('no' and 'yes'), and since the task is to determine if there is chronic heart failure, we only need to include the possible values for each target class. In this case, the possible values for both target classes are just the single category provided ('no' for the no class and 'yes' for the yes class).

Note: If there were other possible values for nr_11, such as 'unknown' or 'not available', we would include them in the dictionary only if they were present in one or more target classes.