human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "The Estimation of Probabilities in Attribute Selection Measures for Decision Tree Induction"):
  Abstract: In this paper we analyze two well-known measures for attribute selection in decision tree induction, informativity and gini index. In particular, we are interested in the influence of different methods for estimating probabilities on these two measures. The results of experiments show that different measures, which are obtained by different probability estimation methods, determine the preferential order of attributes in a given node. Therefore, they determine the structure of a constructed decision tree. This feature can be very beneficial, especially in real-world applications where several different trees are often required.

Includes members:
  Member "Learning Decision Trees from Decision Rules:":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")
  Member "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "On Pruning and Averaging Decision Trees")
  Member "LEARNING FOR DECISION MAKING: The FRD Approach and a Comparative Study  Machine Learning and Inference Laboratory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Rule_Learning