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: "discrete signal processing on graphs sampling theory"
  Abstract: We propose a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory. We show that perfect recovery is possible for graph signals bandlimited under the graph Fourier transform. The sampled signal coefficients form a new graph signal, whose corresponding graph structure preserves the first-order difference of the original graph signal. For general graphs, an optimal sampling operator based on experimentally designed sampling is proposed to guarantee perfect recovery and robustness to noise; for graphs whose graph Fourier transforms are frames with maximal robustness to erasures as well as for Erdős-Renyi graphs, random sampling leads to perfect recovery with high probability. We further establish the connection to the sampling theory of finite discrete-time signal processing and previous work on signal recovery on graphs. To handle full-band graph signals, we propose a graph filter bank based on sampling theory on graphs. Finally, we apply the proposed sampling theory to semi-supervised classification of online blogs and digit images, where we achieve similar or better performance with fewer labeled samples compared to previous work.

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 "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-3 (induced by source paper "discrete signal processing with set functions"):
  with members:
    - "fast iterative combinatorial auctions via bayesian learning"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"
    - "testing coverage functions"

Connected through hyperedge HE-4 (induced by source paper "neural network approximation of graph fourier transforms for sparse sampling of networked flow dynamics"):
  with members:
    - "optimal sampling of water distribution network dynamics using graph fourier transform"
    - "a comprehensive survey on graph neural networks"
    - "greedy sampling of graph signals"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-5 (induced by source paper "processes on metric graphs extrapolation and sampling theorem"):
  with members:
    - "spectrum degeneracy for functions on branching lines and impact on extrapolation and sampling"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "3d point capsule networks"
    - "weighted multi projection 3d point cloud denoising with estimated tangent planes"
    - "a graph cnn for 3d point cloud classification"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-7 (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-8 (induced by source paper "inverse graph learning over optimization networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "interplay between topology and social learning over weak graphs"
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"

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)