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: "subgraph based filterbanks for graph signals"
  Abstract: We design a critically sampled compact-support biorthogonal transform for graph signals, via graph filterbanks. Instead of partitioning the nodes in two sets so as to remove one every two nodes in the filterbank downsampling operations, the design is based on a partition of the graph in connected subgraphs. Coarsening is achieved by defining one “supernode” for each subgraph and the edges for this coarsened graph derives from the connectivity between the subgraphs. Unlike the “one every two nodes” downsampling on bipartite graphs, this coarsening operation does not have an exact formulation in the graph Fourier domain. Instead, we rely on the local Fourier bases of each subgraph to define filtering operations. We apply successfully this method to decompose graph signals and show promising performance on compression and denoising.

Connected through hyperedge HE-1 (induced by source paper "region adaptive graph fourier transform for 3d point clouds"):
  with members:
    - "graph based compression of dynamic 3d point cloud sequences"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-2 (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-3 (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-4 (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"
    - "stationary time vertex signal processing"
    - "sampling of graph signals with successive local aggregations"

Connected through hyperedge HE-5 (induced by source paper "spectral domain sampling of graph signals"):
  with members:
    - "blind identification of graph filters"
    - "spectral projector based graph fourier transforms"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "an m channel critically sampled filter bank for graph signals"

Connected through hyperedge HE-6 (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"
    - "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"
    - "asymptotic justification of bandlimited interpolation of graph signals for semi supervised learning"

Connected through hyperedge HE-7 (induced by source paper "intertwining wavelets or multiresolution analysis on graphs through random forests"):
  with members:
    - "a multiscale pyramid transform for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"

Connected through hyperedge HE-8 (induced by source paper "scalable m channel critically sampled filter banks for graph signals"):
  with members:
    - "approximate fast graph fourier transforms via multilayer sparse approximations"
    - "structured sampling and fast reconstruction of smooth graph signals"
    - "fast eigenspace approximation using random signals"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "an m channel critically sampled filter bank for graph signals"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "signals on graphs uncertainty principle and sampling"
    - "random sampling of bandlimited signals on graphs"

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)