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 "A Comparison of Pruning Methods for Relational Concept Learning"):
  Abstract: Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are more accurate, but much slower, because they have to generate an overly specific concept description first. We have experimented with a variety of pruning methods, including two new methods that try to combine and integrate pre- and post-pruning in order to achieve both accuracy and efficiency. This is verified with test series in a chess position classification task.

Includes members:
  Member "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "On Pruning and Averaging Decision Trees")
  Member "An investigation of noise-tolerant relational concept learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")
  Member "Fossil: A Robust Relational Learner":
    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")
  Member "Top-Down Pruning in Relational Learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")
  Member "Generating Declarative Language Bias for Top-Down ILP Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Top-down Induction of Logical Decision Trees")

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