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: "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"
  Abstract: Wavelet shrinkage,the method proposed by seminal work of Donohoand Johnstone is a disarmingly simple and efficient way of de-noising data. Shrinking wavelet coefficients was proposed from several optimality criteria. The most notable are the asymptotic minimax and cross-validation criteria. In this paper a wavelet shrinkage by imposing natural properties of Bayesian models on data is proposed. The performance of methods are tested on standard Donoho-Johnstone test functions. Key Words and Phrases: Wavelets, Discrete Wavelet Transform, Thresholding, Bayes Model. 1991 AMS Subject Classification: 42A06, 62G07.

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?"
    - "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 Shrinkage: Asymptopia?"
    - "Wavelet Thresholding via a Bayesian Approach"
    - "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 "ESTIMATING THE SQUARE ROOT OF A DENSITY VIA COMPACTLY SUPPORTED WAVELETS"):
  with members:
    - "Density estimation by wavelet thresholding"
    - "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 "The Stationary Wavelet Transform and some Statistical Applications"):
  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"

Connected through hyperedge HE-5 (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,"
    - "Minimax Bayes, asymptotic minimax and sparse wavelet priors"

Connected through hyperedge HE-6 (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,"
    - "The Stationary Wavelet Transform and some Statistical Applications"
    - "DE-NOISING BY reconstruction f n is defined in the wavelet domain by translating all the"
    - "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