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 "Top-Down Pruning in Relational Learning"):
  Abstract: Pruning is an effective method for dealing with noise in Machine Learning. Recently pruning algorithms, in particular Reduced Error Pruning, have also attracted interest in the field of Inductive Logic Programming. However, it has been shown that these methods can be very inefficient, because most of the time is wasted for generating clauses that explain noisy examples and subsequently pruning these clauses. We introduce a new method which searches for good theories in a top-down fashion to get a better starting point for the pruning algorithm. Experiments show that this approach can significantly lower the complexity of the task without losing predictive accuracy.

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 "A Comparison of Pruning Methods for Relational Concept 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")

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