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: "Comparing Methods for Refining Certainty-Factor Rule-Bases"
  Abstract: This paper compares two methods for refining uncertain knowledge bases using propositional certainty-factor rules. The first method, implemented in the Rapture system, employs neural-network training to refine the certainties of existing rules but uses a symbolic technique to add new rules. The second method, based on the one used in the Kbann system, initially adds a complete set of potential new rules with very low certainty and allows neural-network training to filter and adjust these rules. Experimental results indicate that the former method results in significantly faster training and produces much simpler refined rule bases with slightly greater accuracy.

Connected through hyperedge HE-1 (induced by source paper "FONN: Combining First Order Logic with Connectionist Learning"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Learning Controllers for Industrial Robots"

Connected through hyperedge HE-2 (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"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
    - "Modifying Network Architectures for Certainty-Factor Rule-Base Revision"

Connected through hyperedge HE-3 (induced by source paper "On the Informativeness of the DNA Promoter Sequences Domain Theory"):
  with members:
    - "Bias-Driven Revision of Logical Domain Theories"
    - "A Case-Based Reasoning Approach"

Connected through hyperedge HE-4 (induced by source paper "Bias-Driven Revision of Logical Domain Theories"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Tractability of Theory Patching"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "On the Informativeness of the DNA Promoter Sequences Domain Theory"

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