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 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.

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 "Bayesian Case-Based Reasoning with Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Bayesian Prototype Trees by Simulated Annealing")
  Member "Learning Bayesian Prototype Trees by Simulated Annealing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Bayesian Case-Based Reasoning with Neural Networks")
  Member "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962.":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning networks for face analysis and synthesis")

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