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: "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
  Abstract: Mean field methods provide computationally efficient approximations to posterior probability distributions for graphical models. Simple mean field methods make a completely factorized approximation to the posterior, which is unlikely to be accurate when the posterior is multimodal. Indeed, if the posterior is multi-modal, only one of the modes can be captured. To improve the mean field approximation in such cases, we employ mixture models as posterior approximations, where each mixture component is a factorized distribution. We describe efficient methods for optimizing the parameters in these models.

Connected through hyperedge HE-1 (induced by source paper "Factorial Hidden Markov Models"):
  with members:
    - "Structured Representation of Complex Stochastic Systems"
    - "Hidden Markov decision trees"
    - "Coupled hidden Markov models for complex action recognition"
    - "Tractable Inference for Complex Stochastic Processes"
    - "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Speech Recognition with Dynamic Bayesian Networks"

Connected through hyperedge HE-2 (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"
    - "Learning in Boltzmann Trees"
    - "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-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