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: "From Theory Refinement to KB Maintenance: a Position Statement"
  Abstract: Since we consider theory refinement (TR) as a possible key concept for a methodologically clear view of knowledge-base maintenance, we try to give a structured overview about the actual state-of-the-art in TR. This overview is arranged along the description of TR as a search problem. We explain the basic approach, show the variety of existing systems and try to give some hints about the direction future research should go.

Connected through hyperedge HE-1 (induced by source paper "Automated Refinement of First-Order Horn-Clause Domain Theories"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "References Automatic student modeling and bug library construction using theory refinement. Ph.D. ml/ Symbolic revision"
    - "LEARNING CONCEPTS BY ASKING QUESTIONS"

Connected through hyperedge HE-2 (induced by source paper "Multi-Strategy Learning and Theory Revision"):
  with members:
    - "Tractability of Theory Patching"
    - "Knowledge Based Systems"

Connected through hyperedge HE-3 (induced by source paper "Theory Refinement Combining Analytical and Empirical Methods"):
  with members:
    - "An Optimized Theory Revision Module"
    - "The Challenge of Revising an Impure Theory"
    - "Learning Analytically and Inductively"
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "Tractability of Theory Patching"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Extending Theory Refinement to M-of-N Rules"

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