human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "Inductive Logic Programming"):
  with members:
    - "A General Lower Bound on the Number of Examples Needed for Learning"

Connected through hyperedge HE-2 (induced by source paper "Automated Refinement of First-Order Horn-Clause Domain Theories"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "From Theory Refinement to KB Maintenance: a Position Statement"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "References Automatic student modeling and bug library construction using theory refinement. Ph.D. ml/ Symbolic revision"

Connected through hyperedge HE-3 (induced by source paper "Learning First-Order Acyclic Horn Programs from Entailment"):
  with members:
    - "Learning Goal-Decomposition Rules using Exercises"
    - "Learning Horn Definitions with Equivalence and Membership Queries"

Connected through hyperedge HE-4 (induced by source paper "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"):
  with members:
    - "Hierarchical Explanation-Based Reinforcement Learning"
    - "Learning Goal-Decomposition Rules using Exercises"
    - "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"

Connected through hyperedge HE-5 (induced by source paper "Explanations of Empirically Derived Reactive Plans"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"
    - "AN ENHANCER FOR REACTIVE PLANS"

Connected through hyperedge HE-6 (induced by source paper "Observation and Generalisation in a Simulated Robot World"):
  with members:
    (none)

Connected through hyperedge HE-7 (induced by source paper "Relating Relational Learning Algorithms"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "An Improved Algorithm for Incremental Induction of Decision Trees"

Question: Please tell me which class the center node 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