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 Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition"
  Abstract: This paper presents a general framework, learning search-control heuristics for logic programs, which can be used to improve both the efficiency and accuracy of knowledge-based systems expressed as definite-clause logic programs. The approach combines techniques of explanation-based learning and recent advances in inductive logic programming to learn clause-selection heuristics that guide program execution. Two specific applications of this framework are detailed: dynamic optimization of Prolog programs (improving efficiency) and natural language acquisition (improving accuracy). In the area of program optimization, a prototype system, Dolphin is able to transform some intractable specifications into polynomial-time algorithms, and outperforms competing approaches in several benchmark speedup domains. A prototype language acquisition system, Chill is also described. It is capable of automatically acquiring semantic grammars, which uniformly incorprate syntactic and semantic constraints to parse sentences into case-role representations. Initial experiments show that this approach is able to construct accurate parsers which generalize well to novel sentences and significantly outperform previous approaches to learning case-role mapping based on connectionist techniques. Planned extensions of the general framework and the specific applications as well as plans for further evaluation are also discussed.

Connected through hyperedge HE-1 (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"
    - "Combining FOIL and EBG to Speed-up Logic Programs"
    - "Chunking in soar: The anatomy of a general learn ing mechanism. Machine Learning, 1(1). Learning"

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