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: "Ensemble Learning for Hidden Markov Models"
  Abstract: The standard method for training Hidden Markov Models optimizes a point estimate of the model parameters. This estimate, which can be viewed as the maximum of a posterior probability density over the model parameters, may be susceptible to over-fitting, and contains no indication of parameter uncertainty. Also, this maximum may be unrepresentative of the posterior probability distribution. In this paper we study a method in which we optimize an ensemble which approximates the entire posterior probability distribution. The ensemble learning algorithm requires the same resources as the traditional Baum-Welch algorithm. The traditional training algorithm for hidden Markov models is an expectation-maximization (EM) algorithm (Dempster et al. 1977) known as the Baum-Welch algorithm. It is a maximum likelihood method, or, with a simple modification, a penalized maximum likelihood method, which can be viewed as maximizing a posterior probability density over the model parameters. Recently, Hinton and van Camp (1993) developed a technique known as ensemble learning (see also MacKay (1995) for a review). Whereas maximum a posteriori methods optimize a point estimate of the parameters, in ensemble learning an ensemble is optimized, so that it approximates the entire posterior probability distribution over the parameters. The objective function that is optimized is a variational free energy (Feynman 1972) which measures the relative entropy between the approximating ensemble and the true distribution. In this paper we derive and test an ensemble learning algorithm for hidden Markov models, building on Neal

Connected through hyperedge HE-1 (induced by source paper "Choice of Basis for Laplace Approximation"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-2 (induced by source paper "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"):
  with members:
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Ensemble Learning and Evidence Maximization"
    - "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"
    - "Probabilistic Networks: New Models and New Methods"
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-3 (induced by source paper "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Ensemble Learning and Evidence Maximization"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Free Energy Minimization Algorithm for Decoding and Cryptanalysis three binary vectors: s of length N"

Connected through hyperedge HE-4 (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"
    - "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