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: "Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines"
  Abstract: We present a method for automatically determining the structure and the connection weights of a Boltzmann machine corresponding to a given Bayesian network representation of a probability distribution on a set of discrete variables. The resulting Boltzmann machine structure can be implemented efficiently on massively parallel hardware, since the structure can be divided into two separate clusters where all the nodes in one cluster can be updated simultaneously. The updating process of the Boltzmann machine approximates a Gibbs sampling process of the original Bayesian network in the sense that the Boltzmann machine converges to the same final state as the Gibbs sampler does. The mapping from a Bayesian network to a Boltzmann machine can be seen as a method for incorporating probabilistic a priori information into a neural network architecture, which can then be trained further with existing learning algorithms.

Connected through hyperedge HE-1 (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"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "Learning in neural networks with Bayesian prototypes"

Connected through hyperedge HE-2 (induced by source paper "Mapping Bayesian Networks to Boltzmann Machines"):
  with members:
    - "Unsupervised learning of distributions on binary vectors using two layer networks"
    - "Constructing Computationally Efficient Bayesian Models via Unsupervised Clustering  Probabilistic Reasoning and Bayesian Belief Networks,"

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