human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Free energy coding  In")
  Member "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Free Energy Minimization Algorithm for Decoding and Cryptanalysis three binary vectors: s of length N")
  Member "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A Practical Bayesian Framework for Backprop Networks")
  Member "Choice of Basis for Laplace Approximation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Practical Bayesian Framework for Backprop Networks")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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