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: "chebyshev polynomial approximation for distributed signal processing"
  Abstract: Unions of graph Fourier multipliers are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators to the high-dimensional signals collected by sensor networks. The proposed method features approximations of the graph Fourier multipliers by shifted Chebyshev polynomials, whose recurrence relations make them readily amenable to distributed computation. We demonstrate how the proposed method can be used in a distributed denoising task, and show that the communication requirements of the method scale gracefully with the size of the network.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (induced by source paper "graph neural networks with convolutional arma filters"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "on the transferability of spectral graph filters"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "hierarchical graph representation learning with differentiable pooling"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "smoothing graph signals via random spanning forests"):
  with members:
    - "random sampling of bandlimited signals on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "generalized optimization framework for graph based semi supervised learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "learning structural node embeddings via diffusion wavelets"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "inductive representation learning on large graphs"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "learning graph representations with embedding propagation"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "isometric transformation invariant graph based deep neural network"):
  with members:
    - "deformable convolutional networks"
    - "geometric deep learning going beyond euclidean data"
    - "graph based isometry invariant representation learning"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

Connected through hyperedge HE-7 (induced by source paper "learning over multitask graphs part i stability analysis"):
  with members:
    - "learning over multitask graphs part ii performance analysis"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "signals on graphs uncertainty principle and sampling"
    - "network lasso clustering and optimization in large graphs"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-8 (induced by source paper "a graph cnn for 3d point cloud classification"):
  with members:
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "graph based compression of dynamic 3d point cloud sequences"
    - "deep convolutional networks on graph structured data"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "diffusion convolutional neural networks"
    - "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"

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.DC(Distributed, Parallel, and Cluster Computing)