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: "Sample Size Calculations for Smoothing Splines Based on Bayesian Confidence Intervals"
  Abstract: Bayesian confidence intervals of a smoothing spline are often used to distinguish two curves. In this paper, we provide an asymptotic formula for sample size calculations based on Bayesian confidence intervals. Approximations and simulations on special functions indicate that this asymptotic formula is reasonably accurate. Key Words: Bayesian confidence intervals; sample size; smoothing spline. fl Address: Department of Statistics and Applied Probability, University of California, Santa Barbara, CA 93106-3110. Tel.: (805)893-4870. Fax: (805)893-2334. E-mail: yuedong@pstat.ucsb.edu. Supported by the National Institute of Health under Grants R01 EY09946, P60 DK20572 and P30 HD18258.

Connected through hyperedge HE-1 (induced by source paper "Smoothing Spline ANOVA with Component-Wise Bayesian "Confidence Intervals" To Appear, J. Computational and Graphical Statistics"):
  with members:
    - "GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES"

Connected through hyperedge HE-2 (induced by source paper "Backfitting in Smoothing Spline ANOVA"):
  with members:
    - "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"

Connected through hyperedge HE-3 (induced by source paper "GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES"):
  with members:
    - "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: Neural_Networks