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 in neural networks with Bayesian prototypes"
  Abstract: Given a set of samples of a probability distribution on a set of discrete random variables, we study the problem of constructing a good approximative neural network model of the underlying probability distribution. Our approach is based on an unsupervised learning scheme where the samples are first divided into separate clusters, and each cluster is then coded as a single vector. These Bayesian prototype vectors consist of conditional probabilities representing the attribute-value distribution inside the corresponding cluster. Using these prototype vectors, it is possible to model the underlying joint probability distribution as a simple Bayesian network (a tree), which can be realized as a feedforward neural network capable of probabilistic reasoning. In this framework, learning means choosing the size of the prototype set, partitioning the samples into the corresponding clusters, and constructing the cluster prototypes. We describe how the prototypes can be determined, given a partition of the samples, and present a method for evaluating the likelihood of the corresponding Bayesian tree. We also present a greedy heuristic for searching through the space of different partition schemes with different numbers of clusters, aiming at an optimal approximation of the probability distribution.

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"
    - "Learning Bayesian Prototype Trees by Simulated Annealing"
    - "NEULA: A hybrid neural-symbolic expert system shell"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"

Connected through hyperedge HE-2 (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 Bayesian Prototype Trees by Simulated Annealing"
    - "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-3 (induced by source paper "Learning Bayesian Prototype Trees by Simulated Annealing"):
  with members:
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "Induction of Selective Bayesian Classifiers"
    - "Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines"
    - "Bayesian Case-Based Reasoning with Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Learning networks for face analysis and synthesis"
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "A Fast Non-Parametric Density Estimation Algorithm"
    - "Efficient Non-parametric Estimation of Probability Density Functions"

Connected through hyperedge HE-5 (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 Bayesian Prototype Trees by Simulated Annealing"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"

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