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 the Connection Between Stochastic Smoothing, Filtering and Estimation with Incomplete Data"
  Abstract: Connections between stochastic smoothing/filtering and estimation with incomplete data are investigated. It is shown, under the right censoring scheme, that the Kaplan-Meier estimator can be characterized as a moment estimate based on a stochastic filter/smoother(a pseudo-filter/smoother). Motivated by this result, a potentially useful martingale approach for estimation and convergence with incomplete data is proposed: estimators are characterized as pseudo-stochastic smoothers (which sometimes reduce to filters), which are described by a (system of) stochastic integral equation(s); recent results in convergence of stochastic integrals and stochastic differential equations are then applied to address convergence issues. As an illustration, the double censoring problem is revisited under this framework, a closed form estimator is proposed and convergence properties studied. Martingale theory plays a vital role in the entire analysis. This approach is in essence a self-consistency method. 1

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"
    - "On Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst"
    - "Backfitting in Smoothing Spline ANOVA"
    - "Identifiability, Improper Priors and Gibbs Sampling for Generalized Linear Models"

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