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: "Theory of Correlations in Stochastic Neural Networks"
  Abstract: One of the main experimental tools in probing the interactions between neurons has been the measurement of the correlations in their activity. In general, however the interpretation of the observed correlations is difficult, since the correlation between a pair of neurons is influenced not only by the direct interaction between them but also by the dynamic state of the entire network to which they belong. Thus, a comparison between the observed correlations and the predictions from specific model networks is needed. In this paper we develop the theory of neuronal correlation functions in large networks comprising of several highly connected subpopulations, and obeying stochastic dynamic rules. When the networks are in asynchronous states, the cross-correlations are relatively weak, i.e., their amplitude relative to that of the auto-correlations is of order of 1=N , N being the size of the interacting populations. Using the weakness of the cross-correlations, general equations which express the matrix of cross-correlations in terms of the mean neuronal activities, and the effective interaction matrix are presented. The effective interactions are the synaptic efficacies multiplied by the the gain of the postsynaptic neurons. The time-delayed cross-correlation matrix can be expressed as a sum of exponentially decaying modes that correspond to the (non-orthogonal) eigenvectors of the effective interaction matrix. The theory is extended to networks with random connectivity, such as randomly dilute networks. This allows for the comparison between the contribution from the internal common input and that from the direct

Connected through hyperedge HE-1 (induced by source paper "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"):
  with members:
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Analysis of Linsker's application of Hebbian rules to Linear Networks"

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"
    - "Learning in Boltzmann Trees"

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