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 VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
  Abstract: The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the distribution over the unobserved variables. From this perspective, it is easy to justify an incremental variant of the EM algorithm in which the distribution for only one of the unobserved variables is recalculated in each E step. This variant is shown empirically to give faster convergence in a mixture estimation problem. A variant of the algorithm that exploits sparse conditional distributions is also described, and a wide range of other variant algorithms are also seen to be possible.

Connected through hyperedge HE-1 (induced by source paper "Free energy coding  In"):
  with members:
    - "A simple algorithm that discovers efficient perceptual codes"
    - "Bits-back coding software guide"

Connected through hyperedge HE-2 (induced by source paper "DRAFT Cluster-Weighted Modeling for Time Series Prediction and Characterization"):
  with members:
    - "Data-driven Modeling and Synthesis of Acoustical Instruments"

Connected through hyperedge HE-3 (induced by source paper "Ensemble Learning for Hidden Markov Models"):
  with members:
    - "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Choice of Basis for Laplace Approximation"

Connected through hyperedge HE-4 (induced by source paper "A Comparison of New and Old Algorithms for A Mixture Estimation Problem"):
  with members:
    - "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions"
    - "Update rules for parameter estimation in Bayesian networks"
    - "On-Line Portfolio Selection Using Multiplicative Updates"

Connected through hyperedge HE-5 (induced by source paper "A Statistical Approach to Decision Tree Modeling"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Supervised learning from incomplete data via an EM approach"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"

Connected through hyperedge HE-6 (induced by source paper "On Structured Variational Approximations"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-7 (induced by source paper "The Expectation-Maximization Algorithm for MAP Estimation"):
  with members:
    (none)

Connected through hyperedge HE-8 (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"
    - "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"
    - "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"

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