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: "Localist Attractor Networks"
  Abstract: Attractor networks, which map a continuous input space to a discrete output space, are useful for pattern completion, cleaning up noisy or missing features in an input. However, designing a net to have a given set of attractors is notoriously tricky; training procedures are CPU intensive and often produce spurious attractors and ill-conditioned attractor basins. These difficulties occur because each connection in the network participates in the encoding of multiple attractors. We describe an alternative formulation of attractor networks in which the encoding of knowledge is local, not distributed. Although localist attractor nets have similar dynamics to their distributed counterparts, they are much easier to work with and interpret. We propose a statistical formulation of localist attractor net dynamics, which yields a convergence proof and a mathematical interpretation of model parameters. We present simulation experiments that explore the behavior of lo-calist attractor nets, showing that they produce a gang effectthe presence of an attractor enhances the attractor basins of neighboring attractorsand that spurious attractors occur only at points of symmetry in state space.

Connected through hyperedge HE-1 (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"
    - "Computing upper and lower bounds on likelihoods in intractable networks"

Connected through hyperedge HE-2 (induced by source paper "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model"
    - "On Structured Variational Approximations"
    - "The Expectation-Maximization Algorithm for MAP Estimation"
    - "Sequential Update of Bayesian Network Structure"

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