human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "An Efficient Subsumption Algorithm for Inductive Logic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Efficient -Subsumption based on Graph Algorithms")
  Member "Efficient -Subsumption based on Graph Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "An Efficient Subsumption Algorithm for Inductive Logic Programming")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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