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 First-Order Acyclic Horn Programs from Entailment"
  Abstract: In this paper, we consider learning first-order Horn programs from entailment. In particular, we show that any subclass of first-order acyclic Horn programs with constant arity is exactly learnable from equivalence and entailment membership queries provided it allows a polynomial-time subsumption procedure and satisfies some closure conditions. One consequence of this is that first-order acyclic determinate Horn programs with constant arity are exactly learnable from equiv alence and entailment membership queries.

Connected through hyperedge HE-1 (induced by source paper "LEARNING CONCEPTS BY ASKING QUESTIONS"):
  with members:
    - "Inductive Logic Programming"
    - "Automated Refinement of First-Order Horn-Clause Domain Theories"
    - "Relating Relational Learning Algorithms"
    - "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"
    - "Explanations of Empirically Derived Reactive Plans"
    - "Observation and Generalisation in a Simulated Robot World"
    - "The Origins of Inductive Logic Programming: A Prehistoric Tale"

Connected through hyperedge HE-2 (induced by source paper "Learning Horn Definitions with Equivalence and Membership Queries"):
  with members:
    - "Learning Goal-Decomposition Rules using Exercises"

Connected through hyperedge HE-3 (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 Horn Definitions with Equivalence and Membership Queries"

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