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: "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian"
  Abstract: We describe the maximum-likelihood parameter estimation problem and how the Expectation-Maximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical rigor.

Connected through hyperedge HE-1 (induced by source paper "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Rate of Convergence of the Gibbs Sampler by Gaussian Approximation  SUMMARY"
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "Non-linear Models for Time Series Using Mixtures of Experts"
    - "On Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst"
    - "Backfitting in Smoothing Spline ANOVA"
    - "On the Connection Between Stochastic Smoothing, Filtering and Estimation with Incomplete Data"
    - "Identifiability, Improper Priors and Gibbs Sampling for Generalized Linear Models"

Connected through hyperedge HE-2 (induced by source paper "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "CLASSIFICATION USING HIERARCHICAL MIXTURES OF EXPERTS"
    - "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"
    - "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-3 (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"

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