Based on prior knowledge, we can analyze the relationship between the feature "poutcome" and the task of whether the client subscribes to a term deposit. 

This feature represents the outcome of the previous marketing campaign and has categorical values: 'unknown', 'failure', 'other', and 'success'. 

To analyze the relationship, we can examine the distribution of each category within the target classes 'yes' and 'no'. The goal is to identify if certain values of "poutcome" are more common or influential in determining whether the client subscribes to a term deposit.

Here is a possible dictionary representing the relationship:

```json
{
	"no": ["unknown", "failure"],  
	"yes": ["success"]  
}
```

This dictionary indicates that for the target class 'no', the possible values of "poutcome" are 'unknown' and 'failure', while for the target class 'yes', the possible value is 'success'. 

Note that the values 'other' are not included in the dictionary as they are harder to predict based on the given task. However, the lists of each target class are not empty, meeting the requirement.