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 Goal-Decomposition Rules using Exercises"
  Abstract: Exercises are problems ordered in increasing order of difficulty. Teaching problem-solving through exercises is a widely used pedagogic technique. A computational reason for this is that the knowledge gained by solving simple problems is useful in efficiently solving more difficult problems. We adopt this approach of learning from exercises to acquire search-control knowledge in the form of goal-decomposition rules (d-rules). D-rules are first order, and are learned using a new "generalize-and-test" algorithm which is based on inductive logic programming techniques. We demonstrate the feasibility of the approach by applying it in two planning do mains.

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 Horn Definitions with Equivalence and Membership Queries"

Connected through hyperedge HE-2 (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"
    - "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"

Connected through hyperedge HE-3 (induced by source paper "Combining FOIL and EBG to Speed-up Logic Programs"):
  with members:
    - "Learning Semantic Grammars with Constructive Inductive Logic Programming"
    - "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"
    - "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-4 (induced by source paper "Learning Horn Definitions with Equivalence and Membership Queries"):
  with members:
    - "Learning First-Order Acyclic Horn Programs from Entailment"

Connected through hyperedge HE-5 (induced by source paper "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"):
  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"

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