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: "variational splines and paley wiener spaces on combinatorial graphs"
  Abstract: Notions of interpolating variational splines and Paley-Wiener spaces are introduced on a combinatorial graph G. Both of these definitions explore existence of a combinatorial Laplace operator onG. The existence and uniqueness of interpolating variational splines on a graph is shown. As an application of variational splines, the paper presents a reconstruction algorithm of Paley-Wiener functions on graphs from their uniqueness sets.

Connected through hyperedge HE-1 (induced by source paper "graphbgs background subtraction via recovery of graph signals"):
  with members:
    - "background subtraction in real applications challenges current models and future directions"
    - "a fully convolutional neural network for background subtraction of unseen videos"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "a robust background initialization algorithm with superpixel motion detection"
    - "background subtraction with real time semantic segmentation"
    - "mask r cnn"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"

Connected through hyperedge HE-2 (induced by source paper "spectral domain sampling of graph signals"):
  with members:
    - "blind identification of graph filters"
    - "spectral projector based graph fourier transforms"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "an m channel critically sampled filter bank for graph signals"

Connected through hyperedge HE-3 (induced by source paper "a sampling theory perspective of graph based semi supervised learning"):
  with members:
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"
    - "the optimal sample complexity of pac learning"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "asymptotic justification of bandlimited interpolation of graph signals for semi supervised learning"
    - "active semi supervised learning using sampling theory for graph signals"
    - "sampling large data on graphs"

Connected through hyperedge HE-4 (induced by source paper "splines and wavelets on circulant graphs"):
  with members:
    - "sampling and reconstruction of sparse signals on circulant graphs an introduction to graph fri"
    - "estimation of laplacian spectra of direct and strong product graphs"
    - "gspbox a toolbox for signal processing on graphs"
    - "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"
    - "compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"

Connected through hyperedge HE-5 (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-6 (induced by source paper "discrete signal processing on graphs sampling theory"):
  with members:
    - "signal recovery on graphs random versus experimentally designed sampling"
    - "local set based graph signal reconstruction"
    - "a probabilistic interpretation of sampling theory of graph signals"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "signal recovery on graphs variation minimization"
    - "learning parametric dictionaries for signals on graphs"
    - "active semi supervised learning using sampling theory for graph signals"
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-7 (induced by source paper "local set based graph signal reconstruction"):
  with members:
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "learning laplacian matrix in smooth graph signal representations"
    - "active semi supervised learning using sampling theory for graph signals"
    - "a spectral graph uncertainty principle"
    - "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"
    - "localized iterative methods for interpolation in graph structured data"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-8 (induced by source paper "a distributed tracking algorithm for reconstruction of graph signals"):
  with members:
    - "local set based graph signal reconstruction"
    - "a spectral graph uncertainty principle"
    - "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"
    - "localized iterative methods for interpolation in graph structured data"
    - "discrete signal processing on graphs"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"
    - "chebyshev polynomial approximation for distributed signal processing"

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.IT(Information Theory)