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: "Bias-Driven Revision of Logical Domain Theories"
  Abstract: The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revision problem for propositional domain theories. The approach described here, called PTR, uses probabilities associated with domain theory elements to numerically track the ``ow'' of proof through the theory. This allows us to measure the precise role of a clause or literal in allowing or preventing a (desired or undesired) derivation for a given example. This information is used to efficiently locate and repair awed elements of the theory. PTR is proved to converge to a theory which correctly classifies all examples, and shown experimentally to be fast and accurate even for deep theories.

Connected through hyperedge HE-1 (induced by source paper "Comparing Methods for Refining Certainty-Factor Rule-Bases"):
  with members:
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "On the Informativeness of the DNA Promoter Sequences Domain Theory"
    - "FONN: Combining First Order Logic with Connectionist Learning"

Connected through hyperedge HE-2 (induced by source paper "Tractability of Theory Patching"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Multi-Strategy Learning and Theory Revision"

Connected through hyperedge HE-3 (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"
    - "Comparing Methods for Refining Certainty-Factor Rule-Bases"
    - "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-4 (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"
    - "From Theory Refinement to KB Maintenance: a Position Statement"
    - "Tractability of Theory Patching"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"

Connected through hyperedge HE-5 (induced by source paper "On the Informativeness of the DNA Promoter Sequences Domain Theory"):
  with members:
    - "Comparing Methods for Refining Certainty-Factor Rule-Bases"
    - "A Case-Based Reasoning Approach"

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: Theory