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: "semi supervised learning on graphs with generative adversarial nets"
  Abstract: We investigate how generative adversarial nets (GANs) can help semi-supervised learning on graphs. We first provide insights on working principles of adversarial learning over graphs and then present GraphSGAN, a novel approach to semi-supervised learning on graphs. In GraphSGAN, generator and classifier networks play a novel competitive game. At equilibrium, generator generates fake samples in low-density areas between subgraphs. In order to discriminate fake samples from the real, classifier implicitly takes the density property of subgraph into consideration. An efficient adversarial learning algorithm has been developed to improve traditional normalized graph Laplacian regularization with a theoretical guarantee. Experimental results on several different genres of datasets show that the proposed GraphSGAN significantly outperforms several state-of-the-art methods. GraphSGAN can be also trained using mini-batch, thus enjoys the scalability advantage.

Connected through hyperedge HE-1 (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"
    - "graph wavelet neural network"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "graph neural based end to end data association framework for online multiple object tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "relational deep reinforcement learning"
    - "features for multi target multi camera tracking and re identification"
    - "a twofold siamese network for real time object tracking"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "model free tracking with deep appearance and motion features integration"
    - "relational inductive biases deep learning and graph networks"
    - "peernets exploiting peer wisdom against adversarial attacks"

Connected through hyperedge HE-3 (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"
    - "network representation learning a survey"

Connected through hyperedge HE-4 (induced by source paper "graph adversarial training dynamically regularizing based on graph structure"):
  with members:
    - "neural graph collaborative filtering"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adversarial training towards robust multimedia recommender system"
    - "aaane attention based adversarial autoencoder for multi scale network embedding"
    - "hierarchical graph representation learning with differentiable pooling"
    - "adversarial personalized ranking for recommendation"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-5 (induced by source paper "graphmix regularized training of graph neural networks for semi supervised learning"):
  with members:
    - "gmnn graph markov neural networks"
    - "graph u nets"
    - "mixmatch a holistic approach to semi supervised learning"
    - "graph adversarial training dynamically regularizing based on graph structure"
    - "batch virtual adversarial training for graph convolutional networks"
    - "graph neural networks a review of methods and applications"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "dropblock a regularization method for convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "dimensional reweighting graph convolutional networks"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "dual primal graph convolutional networks"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "large scale learnable graph convolutional networks"
    - "adaptive sampling towards fast graph representation learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "distribution induced bidirectional generative adversarial network for graph representation learning"):
  with members:
    - "large scale adversarial representation learning"
    - "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "improved training of wasserstein gans"
    - "adversarial network embedding"
    - "node2vec scalable feature learning for networks"
    - "adversarial feature learning"

Connected through hyperedge HE-8 (induced by source paper "graph wavelet neural network"):
  with members:
    - "dual primal graph convolutional networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "graph based isometry invariant representation learning"
    - "revisiting semi supervised learning with graph embeddings"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

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.SI(Social and Information Networks)