human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "accelerated graph based nonlinear denoising filters"):
  Abstract: Denoising filters, such as bilateral, guided, and total variation filters, applied to images on general graphs may require repeated application if noise is not small enough. We formulate two acceleration techniques of the resulted iterations: conjugate gradient method and Nesterov's acceleration. We numerically show efficiency of the accelerated nonlinear filters for image denoising and demonstrate 2-12 times speed-up, i.e., the acceleration techniques reduce the number of iterations required to reach a given peak signal-to-noise ratio (PSNR) by the above indicated factor of 2-12.

Includes members:
  Member "accelerated graph based spectral polynomial filters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent implementations applications and extensions of the locally optimal block preconditioned conjugate gradient method lobpcg")
  Member "conjugate gradient acceleration of non linear smoothing filters":
    also connected through other hyperedges:
      (none)
  Member "chebyshev and conjugate gradient filters for graph image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accelerated graph based spectral polynomial filters")
  Member "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast high dimensional bilateral and nonlocal means filtering")
  Member "bilateral filter graph spectral interpretation and extensions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "average consensus by graph filtering new approach explicit convergence rate and optimal design")

Question: Please tell me which class the source paper 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)