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: "Modifying Network Architectures for Certainty-Factor Rule-Base Revision"
  Abstract: This paper describes Rapture | a system for revising probabilistic rule bases that converts symbolic rules into a connectionist network, which is then trained via connectionist techniques. It uses a modified version of backpropagation to refine the certainty factors of the rule base, and uses ID3's information-gain heuristic (Quinlan, 1986) to add new rules. Work is currently under way for finding improved techniques for modifying network architectures that include adding hidden units using the UPSTART algorithm (Frean, 1990). A case is made via comparison with fully connected connectionist techniques for keeping the rule base as close to the original as possible, adding new input units only as needed.

Connected through hyperedge HE-1 (induced by source paper "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Extending Theory Refinement to M-of-N Rules"
    - "Bias-Driven Revision of Logical Domain Theories"
    - "Comparing Methods for Refining Certainty-Factor Rule-Bases"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"

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: Neural_Networks