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: "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.

Connected through hyperedge HE-1 (induced by source paper "Bayesian Case-Based Reasoning with Neural Networks"):
  with members:
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "NEULA: A hybrid neural-symbolic expert system shell"
    - "Learning in neural networks with Bayesian prototypes"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"

Connected through hyperedge HE-2 (induced by source paper "Learning in neural networks with Bayesian prototypes"):
  with members:
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "Induction of Selective Bayesian Classifiers"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."

Connected through hyperedge HE-3 (induced by source paper "Induction of Selective Bayesian Classifiers"):
  with members:
    - "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"
    - "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes"
    - "Learning in neural networks with Bayesian prototypes"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"

Connected through hyperedge HE-4 (induced by source paper "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"):
  with members:
    - "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "A Formal Analysis of Case Base Retrieval"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "Learning in neural networks with Bayesian prototypes"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"

Connected through hyperedge HE-5 (induced by source paper "Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines"):
  with members:
    - "Mapping Bayesian Networks to Boltzmann Machines"

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