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: "graphgan graph representation learning with generative adversarial nets"
  Abstract: The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces "fake" samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "mga momentum gradient attack on network"):
  with members:
    - "unsupervised euclidean distance attack on network embedding"
    - "things you may not know about adversarial example a black box adversarial image attack"
    - "adversarial attack on graph structured data"
    - "adversarial training towards robust multimedia recommender system"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "ga based q attack on community detection"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-2 (induced by source paper "learning graph embedding with adversarial training methods"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "a comprehensive survey on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "attributed social network embedding"
    - "semi supervised learning on graphs with generative adversarial nets"

Connected through hyperedge HE-3 (induced by source paper "graph embedding on biomedical networks methods applications and evaluations"):
  with members:
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "graph embedding techniques applications and performance a survey"
    - "universal language model fine tuning for text classification"
    - "network representation learning a survey"
    - "struc2vec learning node representations from structural identity"
    - "predicting multicellular function through multi layer tissue networks"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-4 (induced by source paper "review on graph feature learning and feature extraction techniques for link prediction"):
  with members:
    - "link prediction based on graph neural networks"
    - "dual convolutional neural network for graph of graphs link prediction"
    - "hierarchical graph representation learning with differentiable pooling"
    - "temporally evolving community detection and prediction in content centric networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "temporally evolving community detection and prediction in content centric networks"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-5 (induced by source paper "an end to end neighborhood based interaction model for knowledge enhanced recommendation"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "kb4rec a dataset for linking knowledge bases with recommender systems"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "nais neural attentive item similarity model for recommendation"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-6 (induced by source paper "multi hot compact network embedding"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "cosine compressive network embedding on large scale information networks"
    - "network representation learning a survey"
    - "struc2vec learning node representations from structural identity"
    - "harp hierarchical representation learning for networks"
    - "a survey on network embedding"
    - "learning k way d dimensional discrete code for compact embedding representations"

Connected through hyperedge HE-7 (induced by source paper "gram scalable generative models for graphs with graph attention mechanism"):
  with members:
    - "graph warp module an auxiliary module for boosting the power of graph neural networks"
    - "graph warp module an auxiliary module for boosting the power of graph neural networks in molecular graph analysis"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "neural architecture optimization"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-8 (induced by source paper "semi supervised learning on graphs with generative adversarial nets"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "good semi supervised learning that requires a bad gan"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "triple generative adversarial nets"
    - "learning graph topological features via gan"
    - "adversarial network embedding"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"

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)