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: "Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum fff j g are level-dependent"
  Abstract: We study the problem of estimating the log spectrum of a stationary Gaussian time series by thresholding the empirical wavelet coefficients. We propose the use of thresholds t j;n depending on sample size n, wavelet basis and resolution level j. At fine resolution levels (j = 1; 2; :::), we propose The purpose of this thresholding level is to make the reconstructed log-spectrum as nearly noise-free as possible. In addition to being pleasant from a visual point of view, the noise-free character leads to attractive theoretical properties over a wide range of smoothness assumptions. Previous proposals set much smaller thresholds and did not enjoy these properties. t j;n = ff j log n;

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"
    - "The Stationary Wavelet Transform and some Statistical Applications"

Connected through hyperedge HE-2 (induced by source paper "ESTIMATING THE SQUARE ROOT OF A DENSITY VIA COMPACTLY SUPPORTED WAVELETS"):
  with members:
    - "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"
    - "Density estimation by wavelet thresholding"

Connected through hyperedge HE-3 (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,"
    - "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"

Connected through hyperedge HE-4 (induced by source paper "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"):
  with members:
    - "Minimax Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,"
    - "Wavelet Thresholding via a Bayesian Approach"
    - "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"

Connected through hyperedge HE-5 (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,"
    - "Wavelet Shrinkage: Asymptopia?"

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