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: "sampling filtering and sparse approximations on combinatorial graphs"
  Abstract: In this paper we address sampling and approximation of functions on combinatorial graphs. We develop filtering on graphs by using Schr\"odinger's group of operators generated by combinatorial Laplace operator. Then we construct a sampling theory by proving Poincare and Plancherel-Polya-type inequalities for functions on graphs. These results lead to a theory of sparse approximations on graphs and have potential applications to filtering, denoising, data dimension reduction, image processing, image compression, computer graphics, visualization and learning theory.

Connected through hyperedge HE-1 (induced by source paper "greedy sampling of graph signals"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "adaptive least mean squares estimation of graph signals"
    - "compressive spectral clustering"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-2 (induced by source paper "structured sampling and fast reconstruction of smooth graph signals"):
  with members:
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "subgraph based filterbanks for graph signals"
    - "compressive spectral clustering"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "signal recovery on graphs random versus experimentally designed sampling"
    - "accelerated spectral clustering using graph filtering of random signals"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-3 (induced by source paper "adaptive least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"

Connected through hyperedge HE-4 (induced by source paper "signals on graphs uncertainty principle and sampling"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "toward an uncertainty principle for weighted graphs"
    - "uncertainty principle and sampling of signals defined on graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "near optimal sensor placement for linear inverse problems"
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-5 (induced by source paper "random sampling of bandlimited signals on graphs"):
  with members:
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "subgraph based filterbanks for graph signals"
    - "compressive spectral clustering"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "signal recovery on graphs random versus experimentally designed sampling"
    - "discrete signal processing on graphs sampling theory"
    - "a probabilistic interpretation of sampling theory of graph signals"

Connected through hyperedge HE-6 (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-7 (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"
    - "variational splines and paley wiener spaces on combinatorial graphs"

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)