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 "Stochastically Guided Disjunctive Version Space Learning"):
  Abstract: This paper presents an incremental concept learning approach to identiflcation of concepts with high overall accuracy. The main idea is to address the concept overlap as a central problem when learning multiple descriptions. Many traditional inductive algorithms, as those from the disjunctive version space family considered here, face this problem. The approach focuses on combinations of confldent, possibly overlapping, concepts with an original stochastic complexity formula. The focusing is e-cient because it is organized as a simulated annealing-based beam search. The experiments show that the approach is especially suitable for developing incremental learning algorithms with the following advantages: flrst, it generates highly accurate concepts; second, it overcomes to a certain degree the sensitivity to the order of examples; and third, it handles noisy examples.

Includes members:
  Member "Rule Induction with CN2: Some Recent Improvements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Multi-class problems and discretization in ICL Extended abstract")
  Member "Error Reduction through Learning Multiple Descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms")
  Member "Learning Decision Lists Using Homogeneous Rules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Combining Data Mining and Machine Learning for Effective User Profiling")
  Member "Classifiers: A Theoretical and Empirical Study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Is Consistency Harmful?")

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