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 Acquisition via Knowledge Integration"
  Abstract: In this paper we are concerned with the problem of acquiring knowledge by integration. Our aim is to construct an integrated knowledge base from several separate sources. The need to merge knowledge bases can arise, for example, when knowledge bases are acquired independently from interactions with several domain experts. As opinions of different domain experts may differ, the knowledge bases constructed in this way will normally differ too. A similar problem can also arise whenever separate knowledge bases are generated by learning algorithms. The objective of integration is to construct one system that exploits all the knowledge that is available and has a good performance. The aim of this paper is to discuss the methodology of knowledge integration, describe the implemented system (INTEG.3), and present some concrete results which demonstrate the advantages of this method.

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 Integration and Learning"

Connected through hyperedge HE-2 (induced by source paper "Knowledge Integration and Learning"):
  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