human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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;

Includes members:
  Member "Minimax Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Minimax Risk over l p -Balls for l q -error Key Words. Minimax Decision Theory.")
  Member "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Minimax Risk over l p -Balls for l q -error Key Words. Minimax Decision Theory.")
  Member "The Stationary Wavelet Transform and some Statistical Applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Minimax Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,")
  Member "DE-NOISING BY reconstruction f n is defined in the wavelet domain by translating all the":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Minimax Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,")
  Member "ESTIMATING THE SQUARE ROOT OF A DENSITY VIA COMPACTLY SUPPORTED WAVELETS":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Density estimation by wavelet thresholding")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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