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: "bilateral filter graph spectral interpretation and extensions"
  Abstract: In this paper we study the bilateral filter proposed by Tomasi and Manduchi, as a spectral domain transform defined on a weighted graph. The nodes of this graph represent the pixels in the image and a graph signal defined on the nodes represents the intensity values. Edge weights in the graph correspond to the bilateral filter coefficients and hence are data adaptive. Spectrum of a graph is defined in terms of the eigenvalues and eigenvectors of the graph Laplacian matrix. We use this spectral interpretation to generalize the bilateral filter and propose more flexible and application specific spectral designs of bilateral-like filters. We show that these spectral filters can be implemented with k-iterative bilateral filtering operations and do not require expensive diagonalization of the Laplacian matrix.

Connected through hyperedge HE-1 (induced by source paper "average consensus by graph filtering new approach explicit convergence rate and optimal design"):
  with members:
    - "discrete signal processing on graphs sampling theory"
    - "discrete signal processing on graphs frequency analysis"
    - "a multiscale pyramid transform for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "consensus of multi agent systems with general linear and lipschitz nonlinear dynamics using distributed adaptive protocols"
    - "discrete signal processing on graphs"
    - "chebyshev polynomials in distributed consensus applications"
    - "polynomial filtering for fast convergence in distributed consensus"

Connected through hyperedge HE-2 (induced by source paper "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "uncertainty principle and sampling of signals defined on graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "active semi supervised learning using sampling theory for graph signals"
    - "sampling large data on graphs"
    - "discrete signal processing on graphs frequency analysis"

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

Connected through hyperedge HE-4 (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"
    - "chebyshev and conjugate gradient filters for graph image denoising"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "boosting of image denoising algorithms"):
  with members:
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "image processing using smooth ordering of its patches"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "edge enhancing filters with negative weights"):
  with members:
    - "chebyshev and conjugate gradient filters for graph image denoising"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "conjugate gradient acceleration of non linear smoothing filters"):
  with members:
    - "fast guided filter"
    - "chebyshev and conjugate gradient filters for graph image denoising"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-8 (induced by source paper "chebyshev and conjugate gradient filters for graph image denoising"):
  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"

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)