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: "Efficient -Subsumption based on Graph Algorithms"
  Abstract: The -subsumption problem is crucial to the efficiency of ILP learning systems. We discuss two -subsumption algorithms based on strategies for preselecting suitable matching literals. The class of clauses, for which subsumption becomes polynomial, is a superset of the deterministic clauses. We further map the general problem of -subsumption to a certain problem of finding a clique of fixed size in a graph, and in return show that a specialization of the pruning strategy of the Car-raghan and Pardalos clique algorithm provides a dramatic reduction of the subsumption search space. We also present empirical results for the mesh design data set.

Connected through hyperedge HE-1 (induced by source paper "An Efficient Subsumption Algorithm for Inductive Logic Programming"):
  with members:
    - "What online Machine Learning can do for Knowledge Acquisition A Case Study"
    - "Efficient Algorithms for -Subsumption"
    - "Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples"

Connected through hyperedge HE-2 (induced by source paper "Efficient Algorithms for -Subsumption"):
  with members:
    - "An Efficient Subsumption Algorithm for Inductive Logic Programming"

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