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: "random sampling of bandlimited signals on graphs"
  Abstract: We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performance depends on a parameter called the graph coherence. On the contrary, the second strategy is adaptive but yields optimal results. Indeed, no more than O(k log(k)) measurements are sufficient to ensure an accurate and stable recovery of all k-bandlimited signals. This second strategy is based on a careful choice of the sampling distribution, which can be estimated quickly. Then, we propose a computationally efficient decoder to reconstruct k-bandlimited signals from their samples. We prove that it yields accurate reconstructions and that it is also stable to noise. Finally, we conduct several experiments to test these techniques.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (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-3 (induced by source paper "transferability of spectral graph convolutional neural networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "stability of graph scattering transforms"
    - "stability properties of graph neural networks"
    - "on the transferability of spectral graph filters"
    - "graph convolutional neural networks via scattering"
    - "a comprehensive survey on graph neural networks"
    - "diffusion scattering transforms on graphs"
    - "autoregressive moving average graph filtering"

Connected through hyperedge HE-4 (induced by source paper "sampling of graph signals via randomized local aggregations"):
  with members:
    - "greedy sampling of graph signals"
    - "the dual graph shift operator identifying the support of the frequency domain"
    - "graph based compression of dynamic 3d point cloud sequences"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "discrete signal processing on graphs sampling theory"
    - "gspbox a toolbox for signal processing on graphs"

Connected through hyperedge HE-5 (induced by source paper "smoothing graph signals via random spanning forests"):
  with members:
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "generalized optimization framework for graph based semi supervised learning"
    - "chebyshev polynomial approximation for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "fast resampling of three dimensional point clouds via graphs"):
  with members:
    - "greedy sampling of graph signals"
    - "blind identification of graph filters"
    - "convergence analysis of the information matrix in gaussian belief propagation"
    - "graph based compression of dynamic 3d point cloud sequences"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "compressive spectral clustering"
    - "sampling of graph signals with successive local aggregations"

Connected through hyperedge HE-7 (induced by source paper "graph based ascent algorithms for function maximization"):
  with members:
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "compressive spectral clustering"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "the power of local information in social networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (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"
    - "discrete signal processing on graphs sampling theory"
    - "asymptotic justification of bandlimited interpolation of graph signals for semi supervised learning"

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.SI(Social and Information Networks)