Based on prior knowledge, the feature "native-country" could potentially be correlated with income. Typically, individuals who are native to countries with higher average incomes tend to have higher incomes themselves. However, it is important to note that this is a categorical feature, and within each country there can be significant income disparities. Additionally, the presence of the "?" category suggests missing or unknown data, which might impact the analysis.

To analyze the relationship between "native-country" and the task of earning more than $50,000 per year, we can start by examining the distribution of the feature within each target class. We can then identify countries that seem to have a higher proportion of individuals earning more than $50,000 and countries where the proportion is lower.

Based on this analysis, a possible dictionary output could be:

```json
{
	"no": ["United-States", "?", "Mexico", "Guatemala", "El-Salvador", "Puerto-Rico", "Dominican-Republic", "South", "Columbia", "Haiti", "Vietnam", "Ecuador", "Peru", "Trinadad&Tobago", "Nicaragua", "Poland", "Iran", "Ireland", "Scotland", "Honduras", "Cambodia", "Jamaica", "Laos", "Thailand", "Yugoslavia", "Outlying-US(Guam-USVI-etc)", "Hungary", "Holand-Netherlands", "Greece", "France"],
	"yes": ["United-States", "?", "India", "Philippines", "Germany", "Canada", "England", "Cuba", "China", "Italy", "Japan", "Taiwan", "Iran", "Vietnam", "Hong"]
}
```

It is important to note that this is just one possible analysis based on the given information. Further analysis and feature engineering may be required to make more accurate predictions.