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: "chebyshev and conjugate gradient filters for graph image denoising"
  Abstract: In 3D image/video acquisition, different views are often captured with varying noise levels across the views. In this paper, we propose a graph-based image enhancement technique that uses a higher quality view to enhance a degraded view. A depth map is utilized as auxiliary information to match the perspectives of the two views. Our method performs graph-based filtering of the noisy image by directly computing a projection of the image to be filtered onto a lower dimensional Krylov subspace of the graph Laplacian. We discuss two graph spectral denoising methods: first using Chebyshev polynomials, and second using iterations of the conjugate gradient algorithm. Our framework generalizes previously known polynomial graph filters, and we demonstrate through numerical simulations that our proposed technique produces subjectively cleaner images with about 1-3 dB improvement in PSNR over existing polynomial graph filters.

Connected through hyperedge HE-1 (induced by source paper "accelerated graph based nonlinear denoising filters"):
  with members:
    - "accelerated graph based spectral polynomial filters"
    - "conjugate gradient acceleration of non linear smoothing filters"
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
    - "bilateral filter graph spectral interpretation and extensions"

Connected through hyperedge HE-2 (induced by source paper "accelerated graph based spectral polynomial filters"):
  with members:
    - "fast guided filter"
    - "nonsymmetric preconditioning for conjugate gradient and steepest descent methods 1"
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "bilateral filter graph spectral interpretation and extensions"

Connected through hyperedge HE-3 (induced by source paper "edge enhancing filters with negative weights"):
  with members:
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "localized iterative methods for interpolation in graph structured data"
    - "bilateral filter graph spectral interpretation and extensions"

Connected through hyperedge HE-4 (induced by source paper "conjugate gradient acceleration of non linear smoothing filters"):
  with members:
    - "fast guided filter"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "bilateral filter graph spectral interpretation and extensions"

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)