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: "fast o 1 bilateral filtering using trigonometric range kernels"
  Abstract: It is well-known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not depend on the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithms in the image processing literature. Along with the spatial filter, the edge-preserving bilateral filter [Tomasi1998] involves an additional range kernel. This is used to restrict the averaging to those neighborhood pixels whose intensity are similar or close to that of the pixel of interest. The range kernel operates by acting on the pixel intensities. This makes the averaging process non-linear and computationally intensive, especially when the spatial filter is large. In this paper, we show how the O(1) averaging algorithms can be leveraged for realizing the bilateral filter in constant-time, by using trigonometric range kernels. This is done by generalizing the idea in [Porikli2008] of using polynomial range kernels. The class of trigonometric kernels turns out to be sufficiently rich, allowing for the approximation of the standard Gaussian bilateral filter. The attractive feature of our approach is that, for a fixed number of terms, the quality of approximation achieved using trigonometric kernels is much superior to that obtained in [Porikli2008] using polynomials.

Connected through hyperedge HE-1 (induced by source paper "fast high dimensional kernel filtering"):
  with members:
    - "fast high dimensional bilateral and nonlocal means filtering"
    - "on fast bilateral filtering using fourier kernels"
    - "fast bilateral filtering of vector valued images"
    - "fast and provably accurate bilateral filtering"

Connected through hyperedge HE-2 (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"
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"

Connected through hyperedge HE-3 (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"
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"

Connected through hyperedge HE-4 (induced by source paper "matlab implementation of machine vision algorithm on ballast degradation evaluation"):
  with members:
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"

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"
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
    - "constant time filtering using shiftable 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"
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
    - "constant time filtering using shiftable 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"
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"

Connected through hyperedge HE-8 (induced by source paper "speeding up the bilateral filter a joint acceleration way"):
  with members:
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
    - "constant time filtering using shiftable 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)