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: "gspbox a toolbox for signal processing on graphs"
  Abstract: This document introduces the Graph Signal Processing Toolbox (GSPBox) a framework that can be used to tackle graph related problems with a signal processing approach. It explains the structure and the organization of this software. It also contains a general description of the important modules.

Connected through hyperedge HE-1 (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-2 (induced by source paper "fractional spectral graph wavelets and their applications"):
  with members:
    - "fractional wavelet scattering network and applications"
    - "approximate fast graph fourier transforms via multilayer sparse approximations"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "robust spatial filtering with graph convolutional neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-3 (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"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-4 (induced by source paper "graph wavelet neural network"):
  with members:
    - "dual primal graph convolutional networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "semi supervised learning on graphs with generative adversarial nets"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "graph based isometry invariant representation learning"
    - "revisiting semi supervised learning with graph embeddings"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "unsupervised algorithm for disaggregating low sampling rate electricity consumption of households"):
  with members:
    - "review of smart meter data analytics applications methodologies and challenges"
    - "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"
    - "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"
    - "neural nilm deep neural networks applied to energy disaggregation"
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "non negative sparse coding"

Connected through hyperedge HE-6 (induced by source paper "approximate fast graph fourier transforms via multilayer sparse approximations"):
  with members:
    - "fast orthonormal sparsifying transforms based on householder reflectors"
    - "trainlets dictionary learning in high dimensions"
    - "compressive spectral clustering"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "powers of tensors and fast matrix multiplication"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "dynamic edge conditioned filters in convolutional neural networks on graphs"):
  with members:
    - "orientation boosted voxel nets for 3d object recognition"
    - "gated graph sequence neural networks"
    - "graph based convolutional neural network"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "discriminative embeddings of latent variable models for structured data"
    - "dynamic filter networks"

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"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"

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)