human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "fuzzy thresholding in wavelet domain for speckle reduction in synthetic aperture radar images"
  Abstract: The application of wavelet transforms to Synthetic Aperture Radar (SAR) imagery has improved despeckling performance. To deduce the problem of filtering the multiplicative noise to the case of an additive noise, the wavelet decomposition is performed on the logarithm of the image gray levels. The detail coefficients produced by the bidimensional discrete wavelet transform (DWT-2D) needs to be thresholded to extract out the speckle in highest subbands. An initial threshold value is estimated according to the noise variance. In this paper, an additional fuzzy thresholding approach for automatic determination of the rate threshold level around the traditional wavelet noise thresholding (initial threshold) is applied, and used for the soft or hard-threshold performed on all the high frequency subimages. The filtered logarithmic image is then obtained by reconstruction from the thresholded coefficients. This process is applied a single time, and exclusively to the first level of decomposition. The exponential function of this reconstructed image gives the final filtered image. Experimental results on test images have demonstrated the effectiveness of this method compared to the most of methods in use at the moment.

Connected through hyperedge HE-1 (induced by source paper "quantum spectral analysis frequency in time with applications to signal and image processing"):
  with members:
    - "enhanced directional smoothing algorithm for edge preserving smoothing of synthetic aperture radar images"
    - "kalman s shrinkage for wavelet based despeckling of sar images"
    - "denoising based on wavelets and deblurring via self organizing map for synthetic aperture radar images"
    - "fast cosine transform to increase speed up and efficiency of karhunen loeve transform for lossy image compression"
    - "neural shrinkage for wavelet based sar despeckling"
    - "new wavelet based superresolution algorithm for speckle reduction in sar images"
    - "rule of three for superresolution of still images with applications to compression and denoising"
    - "quantum image representation through two dimensional quantum states and normalized amplitude"

Connected through hyperedge HE-2 (induced by source paper "systholic boolean orthonormalizer network in wavelet domain for sar image despeckling"):
  with members:
    - "enhanced directional smoothing algorithm for edge preserving smoothing of synthetic aperture radar images"
    - "enhanced boolean correlation matrix memory"
    - "kalman s shrinkage for wavelet based despeckling of sar images"
    - "denoising based on wavelets and deblurring via self organizing map for synthetic aperture radar images"
    - "neural shrinkage for wavelet based sar despeckling"
    - "new wavelet based superresolution algorithm for speckle reduction in sar images"

Connected through hyperedge HE-3 (induced by source paper "quantum spectral analysis bandwidth at time"):
  with members:
    - "enhanced directional smoothing algorithm for edge preserving smoothing of synthetic aperture radar images"
    - "denoising based on wavelets and deblurring via self organizing map for synthetic aperture radar images"
    - "fast cosine transform to increase speed up and efficiency of karhunen loeve transform for lossy image compression"
    - "neural shrinkage for wavelet based sar despeckling"
    - "new wavelet based superresolution algorithm for speckle reduction in sar images"
    - "quantum image representation through two dimensional quantum states and normalized amplitude"

Connected through hyperedge HE-4 (induced by source paper "denoising and compression in wavelet domain via projection onto approximation coefficients"):
  with members:
    - "denoising based on wavelets and deblurring via self organizing map for synthetic aperture radar images"
    - "neural shrinkage for wavelet based sar despeckling"

Connected through hyperedge HE-5 (induced by source paper "union is strength in lossy image compression"):
  with members:
    - "denoising based on wavelets and deblurring via self organizing map for synthetic aperture radar images"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)