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: "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
  Abstract: This paper describes Rapture | a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. Rapture uses a modified version of backpropagation to refine the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on refining three actual expert knowledge bases demonstrate that this combined approach generally performs better than previous methods.

Connected through hyperedge HE-1 (induced by source paper "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"):
  with members:
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "Forward models: Supervised learning with a distal teacher"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"
    - "Refining PID Controllers using Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Extending Theory Refinement to M-of-N Rules"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"

Connected through hyperedge HE-3 (induced by source paper "Comparing Methods for Refining Certainty-Factor Rule-Bases"):
  with members:
    - "Bias-Driven Revision of Logical Domain Theories"
    - "On the Informativeness of the DNA Promoter Sequences Domain Theory"
    - "FONN: Combining First Order Logic with Connectionist Learning"

Connected through hyperedge HE-4 (induced by source paper "Revising Bayesian Network Parameters Using Backpropagation"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Automated Refinement of First-Order Horn-Clause Domain Theories"
    - "28 Learning Bayesian Networks Using Feature Selection"

Connected through hyperedge HE-5 (induced by source paper "Modifying Network Architectures for Certainty-Factor Rule-Base Revision"):
  with members:
    (none)

Connected through hyperedge HE-6 (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"
    - "Extending Theory Refinement to M-of-N Rules"

Connected through hyperedge HE-7 (induced by source paper "Bias-Driven Revision of Logical Domain Theories"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Tractability of Theory Patching"
    - "On the Informativeness of the DNA Promoter Sequences Domain Theory"
    - "Comparing Methods for Refining Certainty-Factor Rule-Bases"

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: Probabilistic_Methods