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: "dist gan an improved gan using distance constraints"
  Abstract: We introduce effective training algorithms for Generative Adversarial Networks (GAN) to alleviate mode collapse and gradient vanishing. In our system, we constrain the generator by an Autoencoder (AE). We propose a formulation to consider the reconstructed samples from AE as "real" samples for the discriminator. This couples the convergence of the AE with that of the discriminator, effectively slowing down the convergence of discriminator and reducing gradient vanishing. Importantly, we propose two novel distance constraints to improve the generator. First, we propose a latent-data distance constraint to enforce compatibility between the latent sample distances and the corresponding data sample distances. We use this constraint to explicitly prevent the generator from mode collapse. Second, we propose a discriminator-score distance constraint to align the distribution of the generated samples with that of the real samples through the discriminator score. We use this constraint to guide the generator to synthesize samples that resemble the real ones. Our proposed GAN using these distance constraints, namely Dist-GAN, can achieve better results than state-of-the-art methods across benchmark datasets: synthetic, MNIST, MNIST-1K, CelebA, CIFAR-10 and STL-10 datasets. Our code is published here (this https URL) for research.

Connected through hyperedge HE-1 (induced by source paper "adversarial code learning for image generation"):
  with members:
    - "image transformer"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "large scale gan training for high fidelity natural image synthesis"
    - "mcgan mean and covariance feature matching 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-2 (induced by source paper "autogan neural architecture search for generative adversarial networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "controllable artistic text style transfer via shape matching gan"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "enlightengan deep light enhancement without paired supervision"
    - "the gan landscape losses architectures regularization and normalization"
    - "improving mmd gan training with repulsive loss function"
    - "the relativistic discriminator a key element missing from standard gan"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "self supervised gan analysis and improvement with multi class minimax game"):
  with members:
    - "an improved self supervised gan via adversarial training"
    - "self supervised gans via auxiliary rotation loss"
    - "improving gan with neighbors embedding and gradient matching"
    - "varying k lipschitz constraint for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "rob gan generator discriminator and adversarial attacker"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-4 (induced by source paper "adversarialnas adversarial neural architecture search for gans"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "agan towards automated design of generative adversarial networks"
    - "efficient neural architecture transformation searchin channel level for object detection"
    - "autogan neural architecture search for generative adversarial networks"
    - "one shot neural architecture search via self evaluated template network"
    - "customizable architecture search for semantic segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-5 (induced by source paper "best of many samples distribution matching"):
  with members:
    - "joint wasserstein autoencoders for aligning multimodal embeddings"
    - "learning implicit generative models with the method of learned moments"
    - "mixture density generative adversarial networks"
    - "cramer wold autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "banach wasserstein gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "can vaes generate novel examples"

Connected through hyperedge HE-6 (induced by source paper "an improved self supervised gan via adversarial training"):
  with members:
    - "self supervised gan to counter forgetting"
    - "improving gan with neighbors embedding and gradient matching"
    - "varying k lipschitz constraint for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-7 (induced by source paper "learning to synthesize fashion textures"):
  with members:
    - "diagnosing and enhancing vae models"
    - "spectral normalization for generative adversarial networks"
    - "statistically motivated second order pooling"
    - "viton an image based virtual try on network"
    - "progressive growing of gans for improved quality stability and variation"
    - "texturegan controlling deep image synthesis with texture patches"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-8 (induced by source paper "miso mutual information loss with stochastic style representations for multimodal image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "coloring with words guiding image colorization through text based palette generation"
    - "mine mutual information neural estimation"

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)