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 Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,"
  Abstract: We attempt to recover an unknown function from noisy, sampled data. Using orthonormal bases of compactly supported wavelets we develop a nonlinear method which works in the wavelet domain by simple nonlinear shrinkage of the empirical wavelet coefficients. The shrinkage can be tuned to be nearly minimax over any member of a wide range of Triebel- and Besov-type smoothness constraints, and asymptotically minimax over Besov bodies with p q. Linear estimates cannot achieve even the minimax rates over Triebel and Besov classes with p &lt; 2, so our method can significantly outperform every linear method (kernel, smoothing spline, sieve, : : : ) in a minimax sense. Variants of our method based on simple threshold nonlinearities are nearly minimax. Our method possesses the interpretation of spatial adaptivity: it reconstructs using a kernel which may vary in shape and bandwidth from point to point, depending on the data. Least favorable distributions for certain of the Triebel and Besov scales generate objects with sparse wavelet transforms. Many real objects have similarly sparse transforms, which suggests that these minimax results are relevant for practical problems. Sequels to this paper discuss practical implementation, spatial adaptation properties and applications to inverse problems. Acknowledgements. This work was completed while the first author was on leave from U.C. Berkeley, where his research was supported by NSF DMS 88-10192, by NASA Contract NCA2-488, and by a grant from ATT Foundation. The second author was supported in part by NSF grants DMS 84-51750, 86-00235, and NIH PHS grant GM21215-12. Supersedes an earlier version, titled "Wavelets and Optimal Function Estimation", dated November 10, 1990, and issued as Technical reports by the Departments of Statistics at both Stanford and at U.C. Berkeley.

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:
    - "Wavelet Shrinkage: Asymptopia?"
    - "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 "Space-Frequency Localized Basis Function Networks for Nonlinear System Estimation and Control"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis"
    - "Priors, Stabilizers and Basis Functions: from regularization to radial, tensor and additive splines"
    - "Adaptive Wavelet Control of Nonlinear Systems"

Connected through hyperedge HE-3 (induced by source paper "The Stationary Wavelet Transform and some Statistical Applications"):
  with members:
    - "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"
    - "Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum fff j g are level-dependent"

Connected through hyperedge HE-4 (induced by source paper "Density and hazard rate estimation for right censored data using wavelet methods"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Wavelet Shrinkage: Asymptopia?"):
  with members:
    - "DE-NOISING BY reconstruction f n is defined in the wavelet domain by translating all the"
    - "Minimax Risk over l p -Balls for l q -error Key Words. Minimax Decision Theory."

Connected through hyperedge HE-6 (induced by source paper "Minimax Bayes, asymptotic minimax and sparse wavelet priors"):
  with members:
    - "Wavelet Thresholding via a Bayesian Approach"
    - "Minimax Risk over l p -Balls for l q -error Key Words. Minimax Decision Theory."

Connected through hyperedge HE-7 (induced by source paper "Density estimation by wavelet thresholding"):
  with members:
    - "Minimax Risk over l p -Balls for l q -error Key Words. Minimax Decision Theory."
    - "ESTIMATING THE SQUARE ROOT OF A DENSITY VIA COMPACTLY SUPPORTED WAVELETS"

Connected through hyperedge HE-8 (induced by source paper "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"):
  with members:
    - "Wavelet Thresholding via a Bayesian Approach"
    - "Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum fff j g are level-dependent"
    - "The Stationary Wavelet Transform and some Statistical Applications"
    - "Minimax Risk over l p -Balls for l q -error Key Words. Minimax Decision Theory."
    - "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