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: "graph wavelet neural network"
  Abstract: We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed.

Connected through hyperedge HE-1 (induced by source paper "spectral graph attention network"):
  with members:
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "stabilizing gans with octave convolutions"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "graph neural networks with convolutional arma filters"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "semi supervised graph classification a hierarchical graph perspective"

Connected through hyperedge HE-2 (induced by source paper "graph representation learning via graphical mutual information maximization"):
  with members:
    - "gmnn graph markov neural networks"
    - "a two step graph convolutional decoder for molecule generation"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "demo net degree specific graph neural networks for node and graph classification"
    - "link prediction based on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "regal representation learning based graph alignment"
    - "deep sets"

Connected through hyperedge HE-3 (induced by source paper "self supervised graph representation learning via global context prediction"):
  with members:
    - "self supervised dialogue learning"
    - "gmnn graph markov neural networks"
    - "multi stage self supervised learning for graph convolutional networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "attributed graph clustering via adaptive graph convolution"
    - "grasp2vec learning object representations from self supervised grasping"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "adaptive graph convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "bilinear graph neural network with node interactions"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "hyperbolic graph convolutional neural networks"
    - "neural graph collaborative filtering"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph convolutional networks for text classification"
    - "deep learning on graphs a survey"
    - "rethinking knowledge graph propagation for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "mgcn descriptor learning using multiscale gcns"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "dynamic graph cnn for learning on point clouds"
    - "learning compact geometric features"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "geometric deep learning going beyond euclidean data"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-6 (induced by source paper "human action recognition with multi laplacian graph convolutional networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks via scattering"
    - "a comprehensive survey on graph neural networks"
    - "part based graph convolutional network for action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "predict then propagate graph neural networks meet personalized pagerank"

Connected through hyperedge HE-7 (induced by source paper "fast haar transforms for graph neural networks"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "haarpooling graph pooling with compressive haar basis"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"

Connected through hyperedge HE-8 (induced by source paper "graph transformer networks"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "self attention graph pooling"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "a 2 nets double attention networks"

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.LG(Machine Learning)