Based on prior knowledge, let's analyze the relationship between the feature "FIB_G_POST" and the task of determining whether the patient has chronic heart failure.

The feature "FIB_G_POST" represents whether the patient had ventricular fibrillation at the time of admission to the intensive care unit. To determine if this feature is related to chronic heart failure, we need to analyze how often patients with chronic heart failure have ventricular fibrillation at admission.

To analyze this relationship, we would need a dataset that includes both the "FIB_G_POST" feature and the presence or absence of chronic heart failure. Without this information, it is difficult to establish a direct relationship between the feature and the task.

To create the dictionary as requested, we can assume that the values of "FIB_G_POST" might be related to chronic heart failure, with the following assumption:

- If "FIB_G_POST" is "yes", it is possible that the patient has chronic heart failure.
- If "FIB_G_POST" is "no", it is less likely that the patient has chronic heart failure.

Based on this assumption, the dictionary would look as follows:

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

Please note that since we don't have comprehensive data on the relationship between "FIB_G_POST" and chronic heart failure, we cannot provide an extensive list of values for each target class.