Based on prior knowledge, it is difficult to determine if a person earns more than $50,000 per year based solely on their gender. However, it is still important to analyze the relationship between gender and the target variable.

To generate the required dictionary, we need to analyze the relationship between the "gender" feature and the task of whether a person earns more than $50,000 per year.

Given that the target variable is whether the person earns more than $50,000 per year, we can observe the distribution of gender values for each class (yes and no).

Assuming we have access to a dataset, we can analyze the relationship between gender and the target variable using statistical measures such as chi-square tests or by visualizing the data through plots and graphs.

To create the dictionary, we will separate the gender values into two lists: one for the "no" class and another for the "yes" class. We will only include the gender values for which there is sufficient data.

Here's an example dictionary based on this analysis:

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

This dictionary indicates that both the "no" and "yes" classes include the gender values of "Male" and "Female".