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: "Mapping Bayesian Networks to Boltzmann Machines"
  Abstract: We study the task of tnding a maximal a posteriori (MAP) instantiation of Bayesian network variables, given a partial value assignment as an initial constraint. This problem is known to be NP-hard, so we concentrate on a stochastic approximation algorithm, simulated annealing. This stochastic algorithm can be realized as a sequential process on the set of Bayesian network variables, where only one variable is allowed to change at a time. Consequently, the method can become impractically slow as the number of variables increases. We present a method for mapping a given Bayesian network to a massively parallel Bolztmann machine neural network architecture, in the sense that instead of using the normal sequential simulated annealing algorithm, we can use a massively parallel stochastic process on the Boltzmann machine architecture. The neural network updating process provably converges to a state which solves a given MAP task.

Connected through hyperedge HE-1 (induced by source paper "Constructing Computationally Efficient Bayesian Models via Unsupervised Clustering  Probabilistic Reasoning and Bayesian Belief Networks,"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines"):
  with members:
    - "Learning Bayesian Prototype Trees by Simulated Annealing"

Connected through hyperedge HE-3 (induced by source paper "Unsupervised learning of distributions on binary vectors using two layer networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Unsupervised Learning by Convex and Conic Coding"
    - "Learning in Boltzmann Trees"
    - "Learning Stochastic Feedforward 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