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: "In: Machine Learning, Meta-reasoning and Logics, pp207-232,  Learning from Imperfect Data"
  Abstract: Systems interacting with real-world data must address the issues raised by the possible presence of errors in the observations it makes. In this paper we first present a framework for discussing imperfect data and the resulting problems it may cause. We distinguish between two categories of errors in data random errors or `noise', and systematic errors and examine their relationship to the task of describing observations in a way which is also useful for helping in future problem-solving and learning tasks. Secondly we proceed to examine some of the techniques currently used in AI research for recognising such errors.

Connected through hyperedge HE-1 (induced by source paper "Knowledge Integration and Learning"):
  with members:
    - "Knowledge Acquisition via Knowledge Integration"

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

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