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: "Bayesian Unsupervised Learning of Higher Order Structure"
  Abstract: Multilayer architectures such as those used in Bayesian belief networks and Helmholtz machines provide a powerful framework for representing and learning higher order statistical relations among inputs. Because exact probability calculations with these models are often intractable, there is much interest in finding approximate algorithms. We present an algorithm that efficiently discovers higher order structure using EM and Gibbs sampling. The model can be interpreted as a stochastic recurrent network in which ambiguity in lower-level states is resolved through feedback from higher levels. We demonstrate the performance of the algorithm on bench mark problems.

Connected through hyperedge HE-1 (induced by source paper "A MEAN FIELD LEARNING ALGORITHM FOR UNSUPERVISED NEURAL NETWORKS"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Mean Field Theory for Sigmoid Belief Networks"

Connected through hyperedge HE-2 (induced by source paper "A Brief History of Connectionism"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Learning networks for face analysis and synthesis"
    - "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"
    - "The end of the line for a brain-damaged model of unilateral neglect"

Connected through hyperedge HE-3 (induced by source paper "Mean Field Theory for Sigmoid Belief Networks"):
  with members:
    - "Large Deviation Methods for Approximate Probabilistic Inference, with Rates of Convergence a free parameter. The"
    - "GIBBS-MARKOV MODELS"
    - "GIBBS-MARKOV MODELS"
    - "GIBBS-MARKOV MODELS"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Learning Generative Models with the Up-Propagation Algorithm"
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Localist Attractor 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