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: "A variational approach to Bayesian logistic regression models and their extensions"
  Abstract: We consider a logistic regression model with a Gaussian prior distribution over the parameters. We show that accurate variational techniques can be used to obtain a closed form posterior distribution over the parameters given the data thereby yielding a posterior predictive model. The results are readily extended to (binary) belief networks. For belief networks we also derive closed form posteriors in the presence of missing values. Finally, we show that the dual of the regression problem gives a latent variable density model, the variational formulation of which leads to exactly solvable EM updates.

Connected through hyperedge HE-1 (induced by source paper "Computing upper and lower bounds on likelihoods in intractable networks"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "database"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "Using Qualitative Relationships for Bounding Probability Distributions"

Connected through hyperedge HE-2 (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-3 (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"
    - "Boltzmann Machine learning using mean field theory and linear response correction"

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