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: "adversarial symmetric variational autoencoder"
  Abstract: A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: ($i$) from observed data fed through the encoder to yield codes, and ($ii$) from latent codes drawn from a simple prior and propagated through the decoder to manifest data. Lower bounds are learned for marginal log-likelihood fits observed data and latent codes. When learning with the variational bound, one seeks to minimize the symmetric Kullback-Leibler divergence of joint density functions from ($i$) and ($ii$), while simultaneously seeking to maximize the two marginal log-likelihoods. To facilitate learning, a new form of adversarial training is developed. An extensive set of experiments is performed, in which we demonstrate state-of-the-art data reconstruction and generation on several image benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "double backpropagation for training autoencoders against adversarial attack"):
  with members:
    - "image super resolution as a defense against adversarial attacks"
    - "when does label smoothing help"
    - "adversarial attacks on variational autoencoders"
    - "sparse dnns with improved adversarial robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "deflecting adversarial attacks with pixel deflection"
    - "deep defense training dnns with improved adversarial robustness"

Connected through hyperedge HE-2 (induced by source paper "adversarially approximated autoencoder for image generation and manipulation"):
  with members:
    - "deep visual attention prediction"
    - "neural autoregressive flows"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "video salient object detection via fully convolutional networks"
    - "learning depth from single images with deep neural network embedding focal length"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "implicit deep latent variable models for text generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "neural approaches to conversational ai"
    - "inference suboptimality in variational autoencoders"
    - "generative neural machine translation"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-4 (induced by source paper "mim mutual information machine"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "mine mutual information neural estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "infovae information maximizing variational autoencoders"
    - "searching for activation functions"

Connected through hyperedge HE-5 (induced by source paper "label removed generative adversarial networks incorporating with k means"):
  with members:
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "clustering with deep learning taxonomy and new methods"
    - "deep k means jointly clustering with k means and learning representations"
    - "clustergan latent space clustering in generative adversarial networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "metric learning based generative adversarial network"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-6 (induced by source paper "learning representations by maximizing mutual information in variational autoencoder"):
  with members:
    - "spherical latent spaces for stable variational autoencoders"
    - "recent advances in autoencoder based representation learning"
    - "mine mutual information neural estimation"
    - "adversarially regularized autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "fixing a broken elbo"
    - "towards deeper understanding of variational autoencoding models"
    - "neural discrete representation learning"

Connected through hyperedge HE-7 (induced by source paper "implicit discriminator in variational autoencoder"):
  with members:
    - "improved training of generative adversarial networks using representative features"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "it takes only two adversarial generator encoder networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "invertible conditional gans for image editing"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "mode regularized generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "wyner vae joint and conditional generation with succinct common representation learning"):
  with members:
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "deep variational canonical correlation analysis"
    - "generative models of visually grounded imagination"
    - "deep variational information bottleneck"
    - "adam a method for stochastic optimization"
    - "semi supervised learning with deep generative models"
    - "deep learning face attributes in the wild"
    - "distributed channel synthesis"

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)