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: "Wavelet Shrinkage: Asymptopia?"
  Abstract: Considerable effort has been directed recently to develop asymptotically minimax methods in problems of recovering infinite-dimensional objects (curves, densities, spectral densities, images) from noisy data. A rich and complex body of work has evolved, with nearly- or exactly- minimax estimators being obtained for a variety of interesting problems. Unfortunately, the results have often not been translated into practice, for a variety of reasons sometimes, similarity to known methods, sometimes, computational intractability, and sometimes, lack of spatial adaptivity. We discuss a method for curve estimation based on n noisy data; one translates the empirical wavelet coefficients towards the origin by an amount method is different from methods in common use today, is computationally practical, and is spatially adaptive; thus it avoids a number of previous objections to minimax estimators. At the same time, the method is nearly minimax for a wide variety of loss functions - e.g. pointwise error, global error measured in L p norms, pointwise and global error in estimation of derivatives and for a wide range of smoothness classes, including standard Holder classes, Sobolev classes, and Bounded Variation. This is a much broader near-optimality than anything previously proposed in the minimax literature. Finally, the theory underlying the method is interesting, as it exploits a correspondence between statistical questions and questions of optimal recovery and information-based complexity. Acknowledgements: These results have been described at the Oberwolfach meeting `Mathematische Stochastik' December, 1992 and at the AMS Annual meeting, January 1993. This work was supported by NSF DMS 92-09130. The authors would like to thank Paul-Louis Hennequin, who organized the Ecole d' Ete de Probabilites at Saint Flour 1990, where this collaboration began, and to Universite de Paris VII (Jussieu) and Universite de Paris-sud (Orsay) for supporting visits of DLD and IMJ. The authors would like to thank Ildar Ibragimov and Arkady Nemirovskii for personal correspondence cited below. p

Connected through hyperedge HE-1 (induced by source paper "Minimax Risk over l p -Balls for l q -error Key Words. Minimax Decision Theory."):
  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"
    - "Density estimation by wavelet thresholding"
    - "Minimax Bayes, asymptotic minimax and sparse wavelet priors"

Connected through hyperedge HE-2 (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 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"
    - "The Stationary Wavelet Transform and some Statistical Applications"

Connected through hyperedge HE-3 (induced by source paper "DE-NOISING BY reconstruction f n is defined in the wavelet domain by translating all the"):
  with members:
    - "Minimax Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,"
    - "Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum fff j g are level-dependent"

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