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 "LEARNING CONCEPTS BY ASKING QUESTIONS"):
  Abstract: Tw o important issues in machine learning are explored: the role that memory plays in acquiring new concepts; and the extent to which the learner can take an active part in acquiring these concepts. This chapter describes a program, called Marvin, which uses concepts it has learned previously to learn new concepts. The program forms hypotheses about the concept being learned and tests the hypotheses by asking the trainer questions. Learning begins when the trainer shows Marvin an example of the concept to be learned. The program determines which objects in the example belong to concepts stored in the memory. A description of the new concept is formed by using the information obtained from the memory to generalize the description of the training example. The generalized description is tested when the program constructs new examples and shows these to the trainer, asking if they belong to the target concept.

Includes members:
  Member "Inductive Logic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "Automated Refinement of First-Order Horn-Clause Domain Theories":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "From Theory Refinement to KB Maintenance: a Position Statement")
  Member "Relating Relational Learning Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Rule Induction with CN2: Some Recent Improvements")
  Member "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Hierarchical Explanation-Based Reinforcement Learning")
  Member "Explanations of Empirically Derived Reactive Plans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning Sequential Decision Rules Using Simulation Models and Competition")
  Member "Observation and Generalisation in a Simulated Robot World":
    also connected through other hyperedges:
      (none)
  Member "Learning First-Order Acyclic Horn Programs from Entailment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Learning Horn Definitions with Equivalence and Membership Queries")
  Member "The Origins of Inductive Logic Programming: A Prehistoric Tale":
    also connected through other hyperedges:
      (none)

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: Theory