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: "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
  Abstract: We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoencoder form. For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture. In order to prevent the numerical instability of the network caused by the Laplacian sharpening introduction, we further propose a new numerically stable form of the Laplacian sharpening by incorporating the signed graphs. In addition, a new cost function which finds a latent representation and a latent affinity matrix simultaneously is devised to boost the performance of image clustering tasks. The experimental results on clustering, link prediction and visualization tasks strongly support that the proposed model is stable and outperforms various state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "simple and effective graph autoencoders with one hop linear models"):
  with members:
    - "rwr gae random walk regularization for graph auto encoders"
    - "a degeneracy framework for scalable graph autoencoders"
    - "keep it simple graph autoencoders without graph convolutional networks"
    - "effective decoding in graph auto encoder using triadic closure"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "a comprehensive survey on graph neural networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "constrained graph variational autoencoders for molecule design"

Connected through hyperedge HE-2 (induced by source paper "adaptive graph auto encoder for general data clustering"):
  with members:
    - "attributed graph clustering a deep attentional embedding approach"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "how powerful are graph neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "unsupervised deep embedding for clustering analysis"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "keep it simple graph autoencoders without graph convolutional networks"):
  with members:
    - "gravity inspired graph autoencoders for directed link prediction"
    - "joint embedding of structure and features via graph convolutional networks"
    - "rwr gae random walk regularization for graph auto encoders"
    - "a degeneracy framework for scalable graph autoencoders"
    - "simplifying graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "constrained graph variational autoencoders for molecule design"

Connected through hyperedge HE-4 (induced by source paper "distribution induced bidirectional generative adversarial network for graph representation learning"):
  with members:
    - "large scale adversarial representation learning"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "semi supervised learning on graphs with generative adversarial nets"
    - "improved training of wasserstein gans"
    - "adversarial network embedding"
    - "node2vec scalable feature learning for networks"
    - "adversarial feature learning"

Connected through hyperedge HE-5 (induced by source paper "glmnet graph learning matching networks for feature matching"):
  with members:
    - "learning combinatorial embedding networks for deep graph matching"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "a comprehensive survey on graph neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "semi supervised classification with graph convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "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.LG(Machine Learning)