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: "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.

Connected through hyperedge HE-1 (induced by source paper "Decimatable Boltzmann Machines vs. Gibbs Sampling"):
  with members:
    - "In  Stable Dynamic Parameter Adaptation"

Connected through hyperedge HE-2 (induced by source paper "Boltzmann Machine learning using mean field theory and linear response correction"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "Theory of Correlations in Stochastic Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "Exploiting Tractable Substructures in Intractable Networks"):
  with members:
    - "Hidden Markov decision trees"
    - "On Structured Variational Approximations"
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
    - "Implicit learning in 3D object recognition: The importance of temporal context"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"
    - "Tractable Inference for Complex Stochastic Processes"

Connected through hyperedge HE-4 (induced by source paper "Boltzmann Chains and Hidden Markov Models"):
  with members:
    - "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"
    - "Coupled hidden Markov models for complex action recognition"

Connected through hyperedge HE-5 (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"
    - "Mapping Bayesian Networks to Boltzmann Machines"
    - "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: Neural_Networks