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: "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
  Abstract: A direct implementation of the bilateral filter requires O(σs2) operations per pixel, where σs is the (effective) width of the spatial kernel. A fast implementation of the bilateral filter that required O(1) operations per pixel with respect to σs was recently proposed. This was done by using trigonometric functions for the range kernel of the bilateral filter, and by exploiting their so-called shiftability property. In particular, a fast implementation of the Gaussian bilateral filter was realized by approximating the Gaussian range kernel using raised cosines. Later, it was demonstrated that this idea could be extended to a larger class of filters, including the popular non-local means filter. As already observed, a flip side of this approach was that the run time depended on the width σr of the range kernel. For an image with dynamic range [0,T], the run time scaled as O(T2/σr2) with σr. This made it difficult to implement narrow range kernels, particularly for images with large dynamic range. In this paper, we discuss this problem, and propose some simple steps to accelerate the implementation, in general, and for small σr in particular. We provide some experimental results to demonstrate the acceleration that is achieved using these modifications.

Connected through hyperedge HE-1 (induced by source paper "fast high dimensional bilateral and nonlocal means filtering"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "fast and provably accurate bilateral filtering"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-2 (induced by source paper "fast adaptive bilateral filtering"):
  with members:
    - "speeding up the bilateral filter a joint acceleration way"
    - "on fast bilateral filtering using fourier kernels"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-3 (induced by source paper "matlab implementation of machine vision algorithm on ballast degradation evaluation"):
  with members:
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-4 (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"
    - "chebyshev and conjugate gradient filters for graph image denoising"
    - "bilateral filter graph spectral interpretation and extensions"

Connected through hyperedge HE-5 (induced by source paper "fast bilateral filtering of vector valued images"):
  with members:
    - "on fast bilateral filtering using fourier kernels"
    - "fast and provably accurate bilateral filtering"
    - "constant time filtering using shiftable kernels"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-6 (induced by source paper "on fast bilateral filtering using fourier kernels"):
  with members:
    - "image denoising using optimally weighted bilateral filters a sure and fast approach"
    - "constant time filtering using shiftable kernels"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-7 (induced by source paper "fast and provably accurate bilateral filtering"):
  with members:
    - "fast and accurate bilateral filtering using gauss polynomial decomposition"
    - "image denoising using optimally weighted bilateral filters a sure and fast approach"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-8 (induced by source paper "speeding up the bilateral filter a joint acceleration way"):
  with members:
    - "constant time filtering using shiftable kernels"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

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)