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: "Minimax Bayes, asymptotic minimax and sparse wavelet priors"
  Abstract: Pinsker(1980) gave a precise asymptotic evaluation of the minimax mean squared error of estimation of a signal in Gaussian noise when the signal is known a priori to lie in a compact ellipsoid in Hilbert space. This `Minimax Bayes' method can be applied to a variety of global non-parametric estimation settings with parameter spaces far from ellipsoidal. For example it leads to a theory of exact asymptotic minimax estimation over norm balls in Besov and Triebel spaces using simple co-ordinatewise estimators and wavelet bases. This paper outlines some features of the method common to several applications. In particular, we derive new results on the exact asymptotic minimax risk over weak ` p balls in R n as n ! 1, and also for a class of `local' estimators on the Triebel scale. By its very nature, the method reveals the structure of asymptotically least favorable distributions. Thus we may simulate `least favorable' sample paths. We illustrate this for estimation of a signal in Gaussian white noise over norm balls in certain Besov spaces. In wavelet bases, when p &lt; 2, the least favorable priors are sparse, and the resulting sample paths strikingly different from those observed in Pinsker's ellipsoidal setting (p = 2). Acknowledgements. I am grateful for many conversations with David Donoho and Carl Taswell, and to a referee for helpful comments. This work was supported in part by NSF grants DMS 84-51750, 9209130, and NIH PHS grant GM21215-12.

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,"
    - "Wavelet Shrinkage: Asymptopia?"
    - "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"
    - "Density estimation by wavelet thresholding"

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 Shrinkage: Asymptopia?"
    - "Wavelet Thresholding via a Bayesian Approach"
    - "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"
    - "Density estimation by wavelet thresholding"
    - "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 "Wavelet Thresholding via a Bayesian Approach"):
  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"

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