Based on prior knowledge, we can analyze the relationship between the "safety" feature and the "decision to buy this car" task.

Since the feature "safety" represents the estimated safety of the car and the task is to rate the decision to buy the car, we can assume that the safety feature is an important factor in determining the rating. Generally, cars with higher safety ratings are more likely to receive higher ratings for the decision to buy.

Based on this analysis, here is the dictionary that represents the relationship between the "safety" feature and the target task:

```json
{
	"unacceptable": ["low"],  
	"acceptable": ["low", "med"],  
	"very good": ["high"],  
	"good": ["med", "high"]  
}
```

In this dictionary, we have categorized the safety feature values based on the target classes. Cars with "low" safety ratings are likely to be rated as "unacceptable" to buy. Cars with "low" or "med" safety ratings can be considered "acceptable" to buy. Cars with "high" safety ratings are most likely to be rated as "very good" to buy. Lastly, cars with "med" or "high" safety ratings can be rated as "good" to buy.

Note that values for the target classes are represented as strings in lists. Additionally, we have only included the safety values that are relevant to each target class, ensuring that the list of each target class is not empty.