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: "accelerated spectral clustering using graph filtering of random signals"
  Abstract: We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first k eigenvectors of the similarity matrix' Laplacian, whose computation cost, even for sparse matrices, becomes prohibitive for large datasets. We show that we can estimate the spectral clustering distance matrix without computing these eigenvectors: by graph filtering random signals. Also, we take advantage of the stochasticity of these random vectors to estimate the number of clusters k. We compare our method to classical spectral clustering on synthetic data, and show that it reaches equal performance while being faster by a factor at least two for large datasets.

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 "predicting graph signals using kernel regression where the input signal is agnostic to a graph"):
  with members:
    - "greedy sampling of graph signals"
    - "supervised linear regression for graph learning from graph signals"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-3 (induced by source paper "kernel regression for signals over graphs"):
  with members:
    - "stationary signal processing on graphs"
    - "hilbert transform analytic signal and modulation analysis for graph signal processing"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "graph learning from data under laplacian and structural constraints"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-4 (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"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"
    - "asymptotic justification of bandlimited interpolation of graph signals for semi supervised learning"

Connected through hyperedge HE-5 (induced by source paper "graph sampling with determinantal processes"):
  with members:
    - "greedy sampling of graph signals"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "compressive spectral clustering"
    - "signals on graphs uncertainty principle and sampling"
    - "subsampling for graph power spectrum estimation"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"
    - "a multiscale pyramid transform for graph signals"

Connected through hyperedge HE-6 (induced by source paper "hilbert transform analytic signal and modulation analysis for graph signal processing"):
  with members:
    - "sampling and reconstruction of sparse signals on circulant graphs an introduction to graph fri"
    - "splines and wavelets on circulant graphs"
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "signals on graphs uncertainty principle and sampling"
    - "robust principal component analysis on graphs"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-7 (induced by source paper "compressive spectral clustering"):
  with members:
    - "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"
    - "uncertainty principle and sampling of signals defined on graphs"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"
    - "gspbox a toolbox for signal processing on graphs"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-8 (induced by source paper "compressive k means"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "greedy column subset selection new bounds and distributed algorithms"
    - "compressive spectral clustering"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "a unified framework for approximating and clustering data"
    - "random projections for k means clustering"

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)