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: "DE-NOISING BY reconstruction f n is defined in the wavelet domain by translating all the"
  Abstract: p n. We prove two results about that estimator. [Smooth]: With high probability ^ f fl n is at least as smooth as f , in any of a wide variety of smoothness measures. [Adapt]: The estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. Our proof of these results develops new facts about abstract statistical inference and its connection with Acknowledgements. These results were described at the Symposium on Wavelet Theory, held in connection with the Shanks Lectures at Van-derbilt University, April 3-4 1992. The author would like to thank Professor L.L. Schumaker for hospitality at the conference, and R.A. DeVore, Iain Johnstone, Gerard Kerkyacharian, Bradley Lucier, A.S. Nemirovskii, Ingram Olkin, and Dominique Picard for interesting discussions and correspondence on related topics. The author is also at the University of California, Berkeley

Connected through hyperedge HE-1 (induced by source paper "Minimax Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,"):
  with members:
    - "Space-Frequency Localized Basis Function Networks for Nonlinear System Estimation and Control"
    - "Wavelet Shrinkage: Asymptopia?"
    - "Wavelet Thresholding via a Bayesian Approach"
    - "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"
    - "Density estimation by wavelet thresholding"
    - "Minimax Bayes, asymptotic minimax and sparse wavelet priors"
    - "Density and hazard rate estimation for right censored data using wavelet methods"
    - "Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum fff j g are level-dependent"

Connected through hyperedge HE-2 (induced by source paper "Wavelet Shrinkage: Asymptopia?"):
  with members:
    - "Minimax Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,"
    - "Minimax Risk over l p -Balls for l q -error Key Words. Minimax Decision Theory."

Connected through hyperedge HE-3 (induced by source paper "Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum fff j g are level-dependent"):
  with members:
    - "Minimax Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,"
    - "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"
    - "The Stationary Wavelet Transform and some Statistical Applications"
    - "ESTIMATING THE SQUARE ROOT OF A DENSITY VIA COMPACTLY SUPPORTED WAVELETS"

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