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"
  Abstract: In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph. The resulting signals (data indexed by the nodes) are far removed from time or image signals indexed by well ordered time samples or pixels. DSP, discrete signal processing, provides a comprehensive, elegant, and efficient methodology to describe, represent, transform, analyze, process, or synthesize these well ordered time or image signals. This paper extends to signals on graphs DSP and its basic tenets, including filters, convolution, z-transform, impulse response, spectral representation, Fourier transform, frequency response, and illustrates DSP on graphs by classifying blogs, linear predicting and compressing data from irregularly located weather stations, or predicting behavior of customers of a mobile service provider.

Connected through hyperedge HE-1 (induced by source paper "edgenets edge varying graph neural networks"):
  with members:
    - "dfnets spectral cnns for graphs with feedback looped filters"
    - "graph neural networks with convolutional arma filters"
    - "stability properties of graph neural networks"
    - "on the transferability of spectral graph filters"
    - "generalizing graph convolutional neural networks with edge variant recursions on graphs"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"

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 sampling theory"
    - "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"
    - "testing coverage functions"

Connected through hyperedge HE-4 (induced by source paper "fault location in power distribution systems via deep graph convolutional networks"):
  with members:
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "deep convolutional networks on graph structured data"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-5 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "graph based spatial temporal feature learning for neuromorphic vision sensing"):
  with members:
    - "spatiotemporal filtering for event based action recognition"
    - "graph based object classification for neuromorphic vision sensing"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "attention mechanisms for object recognition with event based cameras"
    - "local spectral graph convolution for point set feature learning"
    - "how powerful are graph neural networks"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "ev flownet self supervised optical flow estimation for event based cameras"

Connected through hyperedge HE-7 (induced by source paper "graph convolutional networks with eigenpooling"):
  with members:
    - "graph u nets"
    - "graph neural networks for social recommendation"
    - "graph representation learning via hard and channel wise attention networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-8 (induced by source paper "powerset convolutional neural networks"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "on the limitations of representing functions on sets"
    - "learning a sat solver from single bit supervision"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep sets"
    - "simplicial closure and higher order link prediction"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "neural message passing for quantum chemistry"

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)