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: "gang of gans generative adversarial networks with maximum margin ranking"
  Abstract: Traditional generative adversarial networks (GAN) and many of its variants are trained by minimizing the KL or JS-divergence loss that measures how close the generated data distribution is from the true data distribution. A recent advance called the WGAN based on Wasserstein distance can improve on the KL and JS-divergence based GANs, and alleviate the gradient vanishing, instability, and mode collapse issues that are common in the GAN training. In this work, we aim at improving on the WGAN by first generalizing its discriminator loss to a margin-based one, which leads to a better discriminator, and in turn a better generator, and then carrying out a progressive training paradigm involving multiple GANs to contribute to the maximum margin ranking loss so that the GAN at later stages will improve upon early stages. We call this method Gang of GANs (GoGAN). We have shown theoretically that the proposed GoGAN can reduce the gap between the true data distribution and the generated data distribution by at least half in an optimally trained WGAN. We have also proposed a new way of measuring GAN quality which is based on image completion tasks. We have evaluated our method on four visual datasets: CelebA, LSUN Bedroom, CIFAR-10, and 50K-SSFF, and have seen both visual and quantitative improvement over baseline WGAN.

Connected through hyperedge HE-1 (induced by source paper "on the evaluation of conditional gans"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "classification accuracy score for conditional generative models"
    - "diversity sensitive conditional generative adversarial networks"
    - "from variational to deterministic autoencoders"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-2 (induced by source paper "breaking the cycle colleagues are all you need"):
  with members:
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "domain partitioning network"):
  with members:
    - "which training methods for gans do actually converge"
    - "multi agent diverse generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "triple generative adversarial nets"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-4 (induced by source paper "venn gan discovering commonalities and particularities of multiple distributions"):
  with members:
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "class distinct and class mutual image generation with gans"
    - "multi agent diverse generative adversarial networks"
    - "modulating early visual processing by language"
    - "progressive growing of gans for improved quality stability and variation"
    - "multi generator generative adversarial nets"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "differentially private generative adversarial networks for time series continuous and discrete open data"):
  with members:
    - "differentially private generative adversarial network"
    - "airline passenger name record generation using generative adversarial networks"
    - "improved training of wasserstein gans"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "membership inference attacks against machine learning models"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-6 (induced by source paper "pros and cons of gan evaluation measures"):
  with members:
    - "decoupled learning for conditional adversarial networks"
    - "cross view image synthesis using conditional gans"
    - "generating adversarial examples with adversarial networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "sampling generative networks"
    - "statistics of deep generated images"

Connected through hyperedge HE-7 (induced by source paper "rankgan a maximum margin ranking gan for generating faces"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "generative multi adversarial networks"
    - "neural photo editing with introspective adversarial networks"
    - "stacked generative adversarial networks"
    - "energy based generative adversarial network"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-8 (induced by source paper "multi agent diverse generative adversarial networks"):
  with members:
    - "learning to protect communications with adversarial neural cryptography"
    - "began boundary equilibrium generative adversarial networks"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "generative multi adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "message passing multi agent gans"
    - "unrolled generative adversarial networks"
    - "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.CV(Computer Vision and Pattern Recognition)