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: "Decimatable Boltzmann Machines vs. Gibbs Sampling"
  Abstract: Exact Boltzmann learning can be done in certain restricted networks by the technique of decimation. We have enlarged the set of dec-imatable Boltzmann machines by introducing a new and more general decimation rule. We have compared solutions of a probability density estimation problem with decimatable Boltzmann machines to the results obtained by Gibbs sampling in unrestricted (non-decimatable)

Connected through hyperedge HE-1 (induced by source paper "Learning in Boltzmann Trees"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Unsupervised learning of distributions on binary vectors using two layer networks"
    - "Boltzmann Chains and Hidden Markov Models"

Connected through hyperedge HE-2 (induced by source paper "In  Stable Dynamic Parameter Adaptation"):
  with members:
    (none)

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