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: "signal representations on graphs tools and applications"
  Abstract: We present a framework for representing and modeling data on graphs. Based on this framework, we study three typical classes of graph signals: smooth graph signals, piecewise-constant graph signals, and piecewise-smooth graph signals. For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties. We then study how such graph dictionary works in two standard tasks: approximation and sampling followed with recovery, both from theoretical as well as algorithmic perspectives. Finally, for each class, we present a case study of a real-world problem by using the proposed methodology.

Connected through hyperedge HE-1 (induced by source paper "towards interpretable sparse graph representation learning with laplacian pooling"):
  with members:
    - "graph convolutional networks with eigenpooling"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graphrnn generating realistic graphs with deep auto regressive models"

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 "detecting localized categorical attributes on graphs"):
  with members:
    - "sampling and reconstruction of sparse signals on circulant graphs an introduction to graph fri"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "distributed hybrid power state estimation under pmu sampling phase errors"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "local set based graph signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "signal recovery on graphs fundamental limits of sampling strategies"):
  with members:
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "subgraph based filterbanks for graph signals"
    - "compressive spectral clustering"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "signal recovery on graphs random versus experimentally designed sampling"
    - "local measurement and reconstruction for noisy graph signals"

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.AI(Artificial Intelligence)