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 Boltzmann Trees"):
  Abstract: We introduce a large family of Boltzmann machines that can be trained using standard gradient descent. The networks can have one or more layers of hidden units, with tree-like connectivity. We show how to implement the supervised learning algorithm for these Boltzmann machines exactly, without resort to simulated or mean-field annealing. The stochastic averages that yield the gradients in weight space are computed by the technique of decimation. We present results on the problems of N -bit parity and the detection of hidden symmetries.

Includes members:
  Member "Exploiting Tractable Substructures in Intractable Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View")
  Member "Boltzmann Machine learning using mean field theory and linear response correction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Theory of Correlations in Stochastic Neural Networks")
  Member "Unsupervised learning of distributions on binary vectors using two layer networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Stochastic Feedforward Networks")
  Member "Boltzmann Chains and Hidden Markov Models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Exploiting Tractable Substructures in Intractable Networks")
  Member "Decimatable Boltzmann Machines vs. Gibbs Sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "In  Stable Dynamic Parameter Adaptation")

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: Neural_Networks