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: "compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs"
  Abstract: This paper extends previous results on wavelet filterbanks for data defined on graphs from the case of orthogonal transforms to more general and flexible biorthogonal transforms. As in the recent work, the construction proceeds in two steps: first we design “one-dimensional” two-channel filterbanks on bipartite graphs, and then extend them to “multi-dimensional” separable two-channel filterbanks for arbitrary graphs via a bipartite subgraph decomposition. We specifically design wavelet filters based on the spectral decomposition of the graph, and state sufficient conditions for the filterbanks to be perfect reconstruction and orthogonal. While our previous designs, referred to as graph-QMF filterbanks, are perfect reconstruction and orthogonal, they are not exactly k-hop localized, i.e., the computation at each node is not localized to a small k-hop neighborhood around the node. In this paper, we relax the condition of orthogonality to design a biorthogonal pair of graph-wavelets that are k-hop localized with compact spectral spread and still satisfy the perfect reconstruction conditions. The design is analogous to the standard Cohen-Daubechies-Feauveau's (CDF) construction of factorizing a maximally-flat Daubechies half-band filter. Preliminary results demonstrate that the proposed filterbanks can be useful for both standard signal processing applications as well as for signals defined on arbitrary graphs.

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 "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-3 (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-4 (induced by source paper "recursive prediction of graph signals with incoming nodes"):
  with members:
    - "predicting graph signals using kernel regression where the input signal is agnostic to a graph"
    - "learning graphs from data a signal representation perspective"
    - "a comprehensive survey on graph neural networks"
    - "greedy sampling of graph signals"
    - "supervised linear regression for graph learning from graph signals"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "signals on graphs uncertainty principle and sampling"

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 "adaptive graph signal processing algorithms and optimal sampling strategies"):
  with members:
    - "greedy sampling of graph signals"
    - "autoregressive moving average graph filtering"
    - "rethinking sketching as sampling a graph signal processing approach"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "adaptive least mean squares estimation of graph signals"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-7 (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"
    - "signal representations on graphs tools and applications"

Connected through hyperedge HE-8 (induced by source paper "kernel regression for signals over graphs"):
  with members:
    - "stationary signal processing on graphs"
    - "hilbert transform analytic signal and modulation analysis for graph signal processing"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "graph learning from data under laplacian and structural constraints"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"

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)