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 Horn Definitions with Equivalence and Membership Queries"
  Abstract: A Horn definition is a set of Horn clauses with the same head literal. In this paper, we consider learning non-recursive, function-free first-order Horn definitions. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Our results have been shown to be applicable to learning efficient goal-decomposition rules in planning domains.

Connected through hyperedge HE-1 (induced by source paper "Learning First-Order Acyclic Horn Programs from Entailment"):
  with members:
    - "LEARNING CONCEPTS BY ASKING QUESTIONS"
    - "Learning Goal-Decomposition Rules using Exercises"

Connected through hyperedge HE-2 (induced by source paper "Learning Goal-Decomposition Rules using Exercises"):
  with members:
    - "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"
    - "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"
    - "Combining FOIL and EBG to Speed-up Logic Programs"
    - "Learning First-Order Acyclic Horn Programs from Entailment"

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