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: "Combining FOIL and EBG to Speed-up Logic Programs"
  Abstract: This paper presents an algorithm that combines traditional EBL techniques and recent developments in inductive logic programming to learn effective clause selection rules for Prolog programs. When these control rules are incorporated into the original program, significant speed-up may be achieved. The algorithm is shown to be an improvement over competing EBL approaches in several domains. Additionally, the algorithm is capable of automatically transforming some intractable algorithms into ones that run in polynomial time.

Connected through hyperedge HE-1 (induced by source paper "An intelligent search method using Inductive Logic Programming"):
  with members:
    - "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"

Connected through hyperedge HE-2 (induced by source paper "Learning Approximate Control Rules Of High Utility"):
  with members:
    - "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"
    - "Chunking in soar: The anatomy of a general learn ing mechanism. Machine Learning, 1(1). Learning"
    - "Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition"

Connected through hyperedge HE-3 (induced by source paper "Learning First-Order Definitions of Functions"):
  with members:
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions"

Connected through hyperedge HE-4 (induced by source paper "Learning Semantic Grammars with Constructive Inductive Logic Programming"):
  with members:
    - "Combining Top-down and Bottom-up Techniques in Inductive Logic Programming"
    - "on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples"
    - "Computational Learning in Humans and Machines"

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"
    - "Learning Goal-Decomposition Rules using Exercises"

Connected through hyperedge HE-6 (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"
    - "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