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 "Learning Bayesian Prototype Trees by Simulated Annealing"):
  Abstract: Given a set of samples of an unknown probability distribution, we study the problem of constructing a good approximative Bayesian network model of the probability distribution in question. This task can be viewed as a search problem, where the goal is to find a maximal probability network model, given the data. In this work, we do not make an attempt to learn arbitrarily complex multi-connected Bayesian network structures, since such resulting models can be unsuitable for practical purposes due to the exponential amount of time required for the reasoning task. Instead, we restrict ourselves to a special class of simple tree-structured Bayesian networks called Bayesian prototype trees, for which a polynomial time algorithm for Bayesian reasoning exists. We show how the probability of a given Bayesian prototype tree model can be evaluated, given the data, and how this evaluation criterion can be used in a stochastic simulated annealing algorithm for searching the model space. The simulated annealing algorithm provably finds the maximal probability model, provided that a sufficient amount of time is used.

Includes members:
  Member "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Formal Analysis of Case Base Retrieval")
  Member "Induction of Selective Bayesian Classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes")
  Member "Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Mapping Bayesian Networks to Boltzmann Machines")
  Member "Bayesian Case-Based Reasoning with Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning in neural networks with Bayesian prototypes")
  Member "Learning in neural networks with Bayesian prototypes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Bayesian Case-Based Reasoning with Neural Networks")

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