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 "Rule Induction with CN2: Some Recent Improvements"):
  Abstract: The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan's C4.5 are also made.

Includes members:
  Member "Stochastically Guided Disjunctive Version Space Learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Error Reduction through Learning Multiple Descriptions")
  Member "Using Qualitative Models to Guide Inductive Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Divide-and-Conquer Approach to Learning from Prior Knowledge")
  Member "Case-Based Sonogram Classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Using Decision Trees to Improve Case-Based Learning")
  Member "Relating Relational Learning Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "LEARNING CONCEPTS BY ASKING QUESTIONS")
  Member "Generalizing from Case Studies: A Case Study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms")
  Member "Incremental Reduced Error Pruning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")
  Member "What do Constructive Learners Really Learn?":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees")
  Member "Unsupervised Constructive Learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in 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