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: "Knowledge Integration and Learning"
  Abstract: LIACC - Technical Report 91-1 Abstract. In this paper we address the problem of acquiring knowledge by integration . Our aim is to construct an integrated knowledge base from several separate sources. The objective of integration is to construct one system that exploits all the knowledge that is available and has good performance. The aim of this paper is to discuss the methodology of knowledge integration and present some concrete results. In our experiments the performance of the integrated theory exceeded the performance of the individual theories by quite a significant amount. Also, the performance did not fluctuate much when the experiments were repeated. These results indicate knowledge integration can complement other existing ML methods.

Connected through hyperedge HE-1 (induced by source paper "In: Machine Learning, Meta-reasoning and Logics, pp207-232,  Learning from Imperfect Data"):
  with members:
    - "Knowledge Acquisition via Knowledge Integration"

Connected through hyperedge HE-2 (induced by source paper "Knowledge Acquisition via Knowledge Integration"):
  with members:
    - "In: Machine Learning, Meta-reasoning and Logics, pp207-232,  Learning from Imperfect Data"

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