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: "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"
  Abstract: Speedup learning is the study of improving the problem-solving performance with experience and from outside guidance. We describe here a system that successfully combines the best features of Explanation-based learning and empirical learning to learn goal decomposition rules from examples of successful problem solving and membership queries. We demonstrate that our system can efficiently learn effective decomposition rules in three different domains. Our results suggest that theory-guided empirical learning can overcome the problems of purely explanation-based learning and purely empirical learning, and be an effective speedup learning method.

Connected through hyperedge HE-1 (induced by source paper "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"):
  with members:
    - "LEARNING CONCEPTS BY ASKING QUESTIONS"
    - "Hierarchical Explanation-Based Reinforcement Learning"
    - "Learning Goal-Decomposition Rules using Exercises"

Connected through hyperedge HE-2 (induced by source paper "Combining FOIL and EBG to Speed-up Logic Programs"):
  with members:
    - "Learning Semantic Grammars with Constructive Inductive Logic Programming"
    - "Learning Goal-Decomposition Rules using Exercises"
    - "Learning Approximate Control Rules Of High Utility"
    - "An intelligent search method using Inductive Logic Programming"
    - "Learning First-Order Definitions of Functions"

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"
    - "Combining FOIL and EBG to Speed-up Logic Programs"
    - "Learning First-Order Acyclic Horn Programs from Entailment"
    - "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: Rule_Learning