Based on prior knowledge, the feature TIME_B_S represents the time elapsed from the beginning of the attack of coronary heart disease (CHD) to the hospital. In this case, we are trying to determine if the patient's myocardial infarction complications show chronic heart failure.

To analyze the relationship between the feature and the task, we need to examine how the different categories of TIME_B_S relate to the presence or absence of chronic heart failure.

To create the dictionary with the required format, we will need to examine the feature values for both the "no" and "yes" target classes.

Here is a possible analysis and dictionary for the given task and feature:

```json
{
	"no": ["2-4 hours", "less than 2 hours", "4-6 hours", "6-8 hours", "8-12 hours", "more than 1 days"],
	"yes": ["more than 2 days", "more than 3 days", "12-24 hours"]
}
```

In this analysis, we identified that patients with chronic heart failure are more likely to have longer time elapsed between the beginning of the CHD attack and hospital admission. Therefore, the "yes" class includes categories with longer time intervals such as "more than 2 days", "more than 3 days", and "12-24 hours".

On the other hand, patients without chronic heart failure (the "no" class) tend to have shorter time intervals between the CHD attack and hospital admission. Therefore, the "no" class includes categories such as "2-4 hours", "less than 2 hours", "4-6 hours", "6-8 hours", "8-12 hours", and "more than 1 days".

Note that not all categories are included in the dictionary, as the question mentions that it is not necessary to include categories that are hard to predict. Nonetheless, the lists for each target class are not empty.