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 Algorithms for -Subsumption"
  Abstract: subsumption is a decidable but incomplete approximation of logic implication, important to inductive logic programming and theorem proving. We show that by context based elimination of possible matches a certain superset of the determinate clauses can be tested for subsumption in polynomial time. We discuss the relation between subsumption and the clique problem, showing in particular that using additional prior knowledge about the substitution space only a small fraction of the search space can be identified as possibly containing globally consistent solutions, which leads to an effective pruning rule. We present empirical results, demonstrating that a combination of both of the above approaches provides an extreme reduction of computational effort.

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

Connected through hyperedge HE-2 (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"
    - "Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples"
    - "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