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: "An Efficient Subsumption Algorithm for Inductive Logic Programming"
  Abstract: In this paper we investigate the efficiency of - subsumption (` ), the basic provability relation in ILP. As D ` C is NP-complete even if we restrict ourselves to linked Horn clauses and fix C to contain only a small constant number of literals, we investigate in several restrictions of D. We first adapt the notion of determinate clauses used in ILP and show that -subsumption is decidable in polynomial time if D is determinate with respect to C. Secondly, we adapt the notion of k-local Horn clauses and show that - subsumption is efficiently computable for some reasonably small k. We then show how these results can be combined, to give an efficient reasoning procedure for determinate k-local Horn clauses, an ILP-problem recently suggested to be polynomial predictable by Cohen (1993) by a simple counting argument. We finally outline how the -reduction algorithm, an essential part of every lgg ILP-learning algorithm, can be im proved by these ideas.

Connected through hyperedge HE-1 (induced by source paper "Efficient -Subsumption based on Graph Algorithms"):
  with members:
    - "Efficient Algorithms for -Subsumption"

Connected through hyperedge HE-2 (induced by source paper "What online Machine Learning can do for Knowledge Acquisition A Case Study"):
  with members:
    - "Cooperation of Data-driven and Model-based Induction Methods for Relational Learning"

Connected through hyperedge HE-3 (induced by source paper "Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Efficient Algorithms for -Subsumption"):
  with members:
    - "Efficient -Subsumption based on Graph Algorithms"

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