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: "Boltzmann Machine learning using mean field theory and linear response correction"
  Abstract: We present a new approximate learning algorithm for Boltzmann Machines, using a systematic expansion of the Gibbs free energy to second order in the weights. The linear response correction to the correlations is given by the Hessian of the Gibbs free energy. The computational complexity of the algorithm is cubic in the number of neurons. We compare the performance of the exact BM learning algorithm with first order (Weiss) mean field theory and second order (TAP) mean field theory. The learning task consists of a fully connected Ising spin glass model on 10 neurons. We conclude that 1) the method works well for paramagnetic problems 2) the TAP correction gives a significant improvement over the Weiss mean field theory, both for paramagnetic and spin glass problems and 3) that the inclusion of diagonal weights improves the Weiss approximation for paramagnetic problems, but not for spin glass problems.

Connected through hyperedge HE-1 (induced by source paper "Theory of Correlations in Stochastic Neural Networks"):
  with members:
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"

Connected through hyperedge HE-2 (induced by source paper "Learning in Boltzmann Trees"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Unsupervised learning of distributions on binary vectors using two layer networks"
    - "Boltzmann Chains and Hidden Markov Models"
    - "Decimatable Boltzmann Machines vs. Gibbs Sampling"

Connected through hyperedge HE-3 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Induction of Multiscale Temporal Structure"
    - "Misclassification Minimization"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "Recursive algorithms for approximating probabilities in graphical models"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "database"
    - "A variational approach to Bayesian logistic regression models and their extensions"

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