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: "enhanced directional smoothing algorithm for edge preserving smoothing of synthetic aperture radar images"
  Abstract: Synthetic aperture radar (SAR) images are subject to prominent speckle noise, which is generally considered a purely multiplicative noise process. In theory, this multiplicative noise is that the ratio of the standard deviation to the signal value, the "coefficient of variation," is theoretically constant at every point in a SAR image. Most of the filters for speckle reduction are based on this property. Such property is irrelevant for the new filter structure, which is based on directional smoothing (DS) theory, the enhanced directional smoothing (EDS) that removes speckle noise from SAR images without blurring edges. We demonstrate the effectiveness of this new filtering method by comparing it to established speckle noise removal techniques on SAR images.

Connected through hyperedge HE-1 (induced by source paper "quantum spectral analysis frequency in time with applications to signal and image processing"):
  with members:
    - "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"
    - "fuzzy thresholding in wavelet domain for speckle reduction in 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 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"
    - "fuzzy thresholding in wavelet domain for speckle reduction in 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:
    - "denoising based on wavelets and deblurring via self organizing map for synthetic aperture radar images"
    - "fuzzy thresholding in wavelet domain for speckle reduction in 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"

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)