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: "On Structured Variational Approximations"
  Abstract: The problem of approximating a probability distribution occurs frequently in many areas of applied mathematics, including statistics, communication theory, machine learning, and the theoretical analysis of complex systems such as neural networks. Saul and Jordan (1996) have recently proposed a powerful method for efficiently approximating probability distributions known as structured variational approximations. In structured variational approximations, exact algorithms for probability computation on tractable substructures are combined with variational methods to handle the interactions between the substructures which make the system as a whole intractable. In this note, I present a mathematical result which can simplify the derivation of struc tured variational approximations in the exponential family of distributions.

Connected through hyperedge HE-1 (induced by source paper "Exploiting Tractable Substructures in Intractable Networks"):
  with members:
    - "Hidden Markov decision trees"
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
    - "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-2 (induced by source paper "Probabilistic Independence Networks for Hidden Markov Probability Models"):
  with members:
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"
    - "Coupled hidden Markov models for complex action recognition"
    - "Tractable Inference for Complex Stochastic Processes"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Belief Propagation and Revision in Networks with Loops"
    - "Speech Recognition with Dynamic Bayesian Networks"

Connected through hyperedge HE-3 (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"
    - "The Expectation-Maximization Algorithm for MAP Estimation"
    - "Localist Attractor Networks"
    - "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: Probabilistic_Methods