human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Theory Refinement Combining Analytical and Empirical Methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Automated Refinement of First-Order Horn-Clause Domain Theories")
  Member "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm")
  Member "Automated Refinement of First-Order Horn-Clause Domain Theories":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "From Theory Refinement to KB Maintenance: a Position Statement")
  Member "28 Learning Bayesian Networks Using Feature Selection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Efficient Learning of Selective Bayesian Network Classifiers")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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