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: "local set based graph signal reconstruction"
  Abstract: Signal processing on graph is attracting more and more attentions. For a graph signal in the low-frequency subspace, the missing data associated with unsampled vertices can be reconstructed through the sampled data by exploiting the smoothness of the graph signal. In this paper, the concept of local set is introduced and two local-set-based iterative methods are proposed to reconstruct bandlimited graph signal from sampled data. In each iteration, one of the proposed methods reweights the sampled residuals for different vertices, while the other propagates the sampled residuals in their respective local sets. These algorithms are built on frame theory and the concept of local sets, based on which several frames and contraction operators are proposed. We then prove that the reconstruction methods converge to the original signal under certain conditions and demonstrate the new methods lead to a significantly faster convergence compared with the baseline method. Furthermore, the correspondence between graph signal sampling and time-domain irregular sampling is analyzed comprehensively, which may be helpful to future works on graph signals. Computer simulations are conducted. The experimental results demonstrate the effectiveness of the reconstruction methods in various sampling geometries, imprecise priori knowledge of cutoff frequency, and noisy scenarios.

Connected through hyperedge HE-1 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs"):
  with members:
    - "an intuitive derivation of the coherence index relation in compressive sensing"
    - "graph filters and the z laplacian"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "hilbert transform analytic signal and modulation analysis for graph signal processing"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "subgraph based filterbanks for graph signals"
    - "stationary time vertex signal processing"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "adaptive graph signal processing algorithms and optimal sampling strategies"):
  with members:
    - "greedy sampling of graph signals"
    - "autoregressive moving average graph filtering"
    - "rethinking sketching as sampling a graph signal processing approach"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "adaptive least mean squares estimation of graph signals"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-5 (induced by source paper "detecting localized categorical attributes on graphs"):
  with members:
    - "sampling and reconstruction of sparse signals on circulant graphs an introduction to graph fri"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "distributed hybrid power state estimation under pmu sampling phase errors"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "signal representations on graphs tools and applications"

Connected through hyperedge HE-6 (induced by source paper "coding method for parallel iterative linear solver"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding for distributed fog computing"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "analog coding of a source with erasures"
    - "on the worst case communication overhead for distributed data shuffling"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-7 (induced by source paper "sampling and reconstruction of sparse signals on circulant graphs an introduction to graph fri"):
  with members:
    - "splines and wavelets on circulant graphs"
    - "sampling of graph signals with successive local aggregations"
    - "discrete signal processing on graphs sampling theory"
    - "estimation of laplacian spectra of direct and strong product graphs"
    - "signal recovery on graphs variation minimization"
    - "active semi supervised learning using sampling theory for graph signals"
    - "on sparse representation in fourier and local bases"
    - "a multiscale pyramid transform for graph signals"

Connected through hyperedge HE-8 (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"
    - "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"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

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)