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: "Revising Bayesian Network Parameters Using Backpropagation"
  Abstract: The problem of learning Bayesian networks with hidden variables is known to be a hard problem. Even the simpler task of learning just the conditional probabilities on a Bayesian network with hidden variables is hard. In this paper, we present an approach that learns the conditional probabilities on a Bayesian network with hidden variables by transforming it into a multi-layer feedforward neural network (ANN). The conditional probabilities are mapped onto weights in the ANN, which are then learned using standard backpropagation techniques. To avoid the problem of exponentially large ANNs, we focus on Bayesian networks with noisy-or and noisy-and nodes. Experiments on real world classification problems demonstrate the effectiveness of our technique.

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"
    - "From Theory Refinement to KB Maintenance: a Position Statement"
    - "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 "28 Learning Bayesian Networks Using Feature Selection"):
  with members:
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"

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"
    - "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"
    - "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"
    - "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: Probabilistic_Methods