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 "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"):
  Abstract: In this article we investigate the relationship between the two popular algorithms, the EM algorithm and the Gibbs sampler. We show that the approximate rate of convergence of the Gibbs sampler by Gaussian approximation is equal to that of the corresponding EM type algorithm. This helps in implementing either of the algorithms as improvement strategies for one algorithm can be directly transported to the other. In particular, by running the EM algorithm we know approximately how many iterations are needed for convergence of the Gibbs sampler. We also obtain a result that under conditions, the EM algorithm used for finding the maximum likelihood estimates can be slower to converge than the corresponding Gibbs sampler for Bayesian inference which uses proper prior distributions. We illustrate our results in a number of realistic examples all based on the generalized linear mixed models.

Includes members:
  Member "Hierarchical Mixtures of Experts and the EM Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Forecasting electricity demand using nonlinear mixture of experts")
  Member "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms")
  Member "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "On Convergence Properties of the EM Algorithm for Gaussian Mixtures")
  Member "On Convergence Properties of the EM Algorithm for Gaussian Mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian")
  Member "Non-linear Models for Time Series Using Mixtures of Experts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Hierarchical Mixtures of Experts and the EM Algorithm")
  Member "On Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "On Convergence Properties of the EM Algorithm for Gaussian Mixtures")
  Member "Backfitting in Smoothing Spline ANOVA":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Sample Size Calculations for Smoothing Splines Based on Bayesian Confidence Intervals")
  Member "On the Connection Between Stochastic Smoothing, Filtering and Estimation with Incomplete Data":
    also connected through other hyperedges:
      (none)

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