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 Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst"
  Abstract: We address the problem of computing the largest fraction of missing information for the EM algorithm and the worst linear function for data augmentation. These are the largest eigenvalue and its associated eigenvector for the Jacobian of the EM operator at a maximum likelihood estimate, which are important for assessing convergence in iterative simulation. An estimate of the largest fraction of missing information is available from the EM iterates; this is often adequate since only a few figures of accuracy are needed. In some instances the EM iteration also gives an estimate of the worst linear function. We show that the power method for eigencomputation can be used to compute efficient and accurate estimates of both quantities. Unlike eigenvalue decomposition, the power method computes only the largest eigenvalue and eigenvector of a matrix, it can take advantage of a good eigenvector estimate as an initial value and it can be terminated after only a few figures of accuracy are obtained. Moreover, the matrix products needed in the power method can be computed by extrapolation, obviating the need to form the Jacobian of the EM operator. We give results of simultation studies on multivariate normal data showing this approach becomes more efficient as the data dimension increases than methods that use a finite-difference approximation to the Jacobian, which is the only general-purpose alternative available. fl Funded by National Institutes of Health Small Business Innovation Reseach Grant 5R44CA65147-03, and by Office of Naval Research contracts N00014-96-1-0192 and N00014-96-1-0330. We are indebted to Tim Hesterberg, Jim Schimert, Doug Clarkson, Anne Greenbaum, and Adrian Raftery for comments and discussion that helped advance this research and improve this paper.

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"
    - "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian"
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "Non-linear Models for Time Series Using Mixtures of Experts"
    - "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"
    - "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"
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "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