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: "Smoothing Spline ANOVA with Component-Wise Bayesian "Confidence Intervals" To Appear, J. Computational and Graphical Statistics"
  Abstract: We study a multivariate smoothing spline estimate of a function of several variables, based on an ANOVA decomposition as sums of main effect functions (of one variable), two-factor interaction functions (of two variables), etc. We derive the Bayesian "confidence intervals" for the components of this decomposition and demonstrate that, even with multiple smoothing parameters, they can be efficiently computed using the publicly available code RKPACK, which was originally designed just to compute the estimates. We carry out a small Monte Carlo study to see how closely the actual properties of these component-wise confidence intervals match their nominal confidence levels. Lastly, we analyze some lake acidity data as a function of calcium concentration, latitude, and longitude, using both polynomial and thin plate spline main effects in the same model.

Connected through hyperedge HE-1 (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"
    - "Backfitting in Smoothing Spline ANOVA"

Connected through hyperedge HE-2 (induced by source paper "GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES"):
  with members:
    - "Sample Size Calculations for Smoothing Splines Based on Bayesian Confidence Intervals"

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: Neural_Networks