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: "adversarially regularized graph autoencoder for graph embedding"
  Abstract: Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding, two variants of adversarial approaches, adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction, graph clustering, and graph visualization tasks.

Connected through hyperedge HE-1 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding"):
  with members:
    - "simple and effective graph autoencoders with one hop linear models"
    - "gravity inspired graph autoencoders for directed link prediction"
    - "a degeneracy framework for scalable graph autoencoders"
    - "effective decoding in graph auto encoder using triadic closure"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "semi implicit graph variational auto encoders"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (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"
    - "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "graph inference learning for semi supervised classification"):
  with members:
    - "gaussian induced convolution for graphs"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "topology adaptive graph convolutional networks"
    - "learning to learn meta critic networks for sample efficient learning"
    - "shift aggregate extract networks"
    - "scene graph generation by iterative message passing"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "comparison of syntactic and semantic representations of programs in neural embeddings"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "code2vec learning distributed representations of code"
    - "naps natural program synthesis dataset"
    - "neural guided deductive search for real time program synthesis from examples"
    - "neural program search solving programming tasks from description and examples"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "neural guided constraint logic programming for program synthesis"
    - "program synthesis using conflict driven learning"

Connected through hyperedge HE-5 (induced by source paper "bipartite link prediction based on topological features via 2 hop path"):
  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"
    - "bipartite graph neural networks for efficient node representation learning"
    - "a comprehensive survey on graph neural networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "adaptive graph auto encoder for general data clustering"):
  with members:
    - "attributed graph clustering a deep attentional embedding approach"
    - "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
    - "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-7 (induced by source paper "embedding graph auto encoder with joint clustering via adjacency sharing"):
  with members:
    - "a comprehensive survey on graph neural 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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "a block based generative model for attributed networks embedding"):
  with members:
    - "attributed social network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "stochastic block model and exploratory analysis in signed networks"
    - "deepwalk online learning of social representations"
    - "efficient community detection in large networks using content and links"

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)