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: "generative modeling using the sliced wasserstein distance"
  Abstract: Generative Adversarial Nets (GANs) are very successful at modeling distributions from given samples, even in the high-dimensional case. However, their formulation is also known to be hard to optimize and often not stable. While this is particularly true for early GAN formulations, there has been significant empirically motivated and theoretically founded progress to improve stability, for instance, by using the Wasserstein distance rather than the Jenson-Shannon divergence. Here, we consider an alternative formulation for generative modeling based on random projections which, in its simplest form, results in a single objective rather than a saddle-point formulation. By augmenting this approach with a discriminator we improve its accuracy. We found our approach to be significantly more stable compared to even the improved Wasserstein GAN. Further, unlike the traditional GAN loss, the loss formulated in our method is a good measure of the actual distance between the distributions and, for the first time for GAN training, we are able to show estimates for the same.

Connected through hyperedge HE-1 (induced by source paper "learning implicit generative models with theoretical guarantees"):
  with members:
    - "deep generative learning via variational gradient flow"
    - "sinkhorn autoencoders"
    - "wasserstein wasserstein auto encoders"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "spectral normalization for generative adversarial networks"
    - "monge amp ere flow for generative modeling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "realistic river image synthesis using deep generative adversarial networks"):
  with members:
    - "decentralized flood forecasting using deep neural networks"
    - "how good is my gan"
    - "a style based generator architecture for generative adversarial networks"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial generation of training examples applications to moving vehicle license plate recognition"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "sample based distributional policy gradient"):
  with members:
    - "gan based deep distributional reinforcement learning for resource management in network slicing"
    - "deepmind control suite"
    - "quota the quantile option architecture for reinforcement learning"
    - "nonlinear distributional gradient temporal difference learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "distributional multivariate policy evaluation and exploration with the bellman gan"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "co generation with gans using ais based hmc"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "mcgan mean and covariance feature matching gan"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-5 (induced by source paper "sliced wasserstein discrepancy for unsupervised domain adaptation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "variational wasserstein clustering"
    - "learning to adapt structured output space for semantic segmentation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "open set domain adaptation by backpropagation"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "sliced wasserstein generative models"):
  with members:
    - "video to video synthesis"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "banach wasserstein gan"
    - "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-7 (induced by source paper "max sliced wasserstein distance and its use for gans"):
  with members:
    - "sliced wasserstein generative models"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "which training methods for gans do actually converge"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "learning gans and ensembles using discrepancy"):
  with members:
    - "teacher student compression with generative adversarial networks"
    - "efficient gan based anomaly detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

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)