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: "constant time filtering using shiftable kernels"
  Abstract: It was recently demonstrated in that the nonlinear bilateral filter can be efficiently implemented using a constant-time or O(1) algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in can be extended to few other linear and nonlinear filters . While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in , we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. This has a two-fold advantage. First, we can use fast recursive algorithms for computing the moving sum , , and, secondly, we can use parallel computation to further speed up the computation. We also show how shiftable kernels can also be used to approximate the (nonlinearshiftable) Gaussian kernel that is ubiquitously used in image filtering.

Connected through hyperedge HE-1 (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"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-2 (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"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-3 (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"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-4 (induced by source paper "fast and accurate bilateral filtering using gauss polynomial decomposition"):
  with members:
    - "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"
    - "fast o 1 bilateral filtering using trigonometric range kernels"

Connected through hyperedge HE-5 (induced by source paper "acceleration of the shiftable mbi o 1 algorithm for bilateral filtering and nonlocal means"):
  with members:
    - "fast o 1 bilateral filtering using trigonometric range kernels"
    - "fast space variant elliptical filtering using box splines"

Connected through hyperedge HE-6 (induced by source paper "improvements on fast space variant elliptical filtering using box splines"):
  with members:
    - "a radial version of the central limit theorem"
    - "fast space variant elliptical filtering using box splines"

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)