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 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.

Connected through hyperedge HE-1 (induced by source paper "Non-linear Models for Time Series Using Mixtures of Experts"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"

Connected through hyperedge HE-2 (induced by source paper "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"

Connected through hyperedge HE-3 (induced by source paper "Backfitting in Smoothing Spline ANOVA"):
  with members:
    - "Sample Size Calculations for Smoothing Splines Based on Bayesian Confidence Intervals"

Connected through hyperedge HE-4 (induced by source paper "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian"
    - "CLASSIFICATION USING HIERARCHICAL MIXTURES OF EXPERTS"
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "On Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst"
    - "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions"

Connected through hyperedge HE-5 (induced by source paper "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY"):
  with members:
    - "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review"
    - "A simulation approach to convergence rates for Markov chain Monte Carlo algorithms"
    - "Convergence properties of perturbed Markov chains"
    - "Assessing Convergence of Markov Chain Monte Carlo Algorithms"
    - "Rates of convergence of the Hastings and Metropolis algorithms"

Connected through hyperedge HE-6 (induced by source paper "Hierarchical Mixtures of Experts and the EM Algorithm"):
  with members:
    - "Validation of Average Error Rate Over Classifiers"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Supervised learning from incomplete data via an EM approach"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On learning hierarchical classifications"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"

Connected through hyperedge HE-7 (induced by source paper "On the Connection Between Stochastic Smoothing, Filtering and Estimation with Incomplete Data"):
  with members:
    (none)

Connected through hyperedge HE-8 (induced by source paper "On Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst"):
  with members:
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"

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