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: "Backfitting in Smoothing Spline ANOVA"
  Abstract: A scheme to compute smoothing spline ANOVA estimates for large data sets with a (near) tensor-product structure is proposed. Such data sets are common in spatial-temporal analysis and image analysis. This scheme combines backfitting algorithm with iterative imputation algorithm in order to save both computational space and time. The convergence of this algorithm and various ways to further speed it up, such as collapsing component functions and successive over-relaxation, are discussed. Issues related to its application in spatial-temporal analysis are discussed too. An application to a global analysis of historical surface temperature data is described.

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"
    - "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 "Sample Size Calculations for Smoothing Splines Based on Bayesian Confidence Intervals"):
  with members:
    - "GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES"
    - "Smoothing Spline ANOVA with Component-Wise Bayesian "Confidence Intervals" To Appear, J. Computational and Graphical Statistics"

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