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: "quantitatively evaluating gans with divergences proposed for training"
  Abstract: Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. However, we currently lack quantitative methods for model assessment. Because of this, while many GAN variants are being proposed, we have relatively little understanding of their relative abilities. In this paper, we evaluate the performance of various types of GANs using divergence and distance functions typically used only for training. We observe consistency across the various proposed metrics and, interestingly, the test-time metrics do not favour networks that use the same training-time criterion. We also compare the proposed metrics to human perceptual scores.

Connected through hyperedge HE-1 (induced by source paper "object centric image generation from layouts"):
  with members:
    - "interactive image generation using scene graphs"
    - "scene graph generation with external knowledge and image reconstruction"
    - "object driven text to image synthesis via adversarial training"
    - "image synthesis from reconfigurable layout and style"
    - "pastegan a semi parametric method to generate image from scene graph"
    - "locality and compositionality in zero shot learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"

Connected through hyperedge HE-2 (induced by source paper "towards gan benchmarks which require generalization"):
  with members:
    - "a call for clarity in reporting bleu scores"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "deep reinforcement learning that matters"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-3 (induced by source paper "deep learning for molecular design a review of the state of the art"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "adversarial autoencoders with constant curvature latent manifolds"
    - "conditional molecular design with deep generative models"
    - "decoding molecular graph embeddings with reinforcement learning"
    - "mol cyclegan a generative model for molecular optimization"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"
    - "the gan landscape losses architectures regularization and normalization"

Connected through hyperedge HE-4 (induced by source paper "fair generative modeling via weak supervision"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "scalable fair clustering"
    - "counterpoint by convolution"
    - "fair clustering through fairlets"
    - "datasheets for datasets"
    - "what is the effect of importance weighting in deep learning"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "learning beyond human expertise with generative models for dental restorations"

Connected through hyperedge HE-5 (induced by source paper "justifying diagnosis decisions by deep neural networks"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "generating continuous representations of medical texts"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

Connected through hyperedge HE-6 (induced by source paper "training generative adversarial networks from incomplete observations using factorised discriminators"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "gain missing data imputation using generative adversarial nets"
    - "spectral normalization for generative adversarial networks"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "a style based generator architecture for generative adversarial networks"
    - "learning image to image translation using paired and unpaired training samples"
    - "triangle generative adversarial networks"
    - "unsupervised audio source separation via spectrum energy preserved wasserstein learning"

Connected through hyperedge HE-7 (induced by source paper "training discriminative models to evaluate generative ones"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "began boundary equilibrium generative adversarial networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "safer classification by synthesis"
    - "rendergan generating realistic labeled data"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "revisiting precision and recall definition for generative model evaluation"):
  with members:
    - "detecting overfitting of deep generative networks via latent recovery"
    - "large scale gan training for high fidelity natural image synthesis"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "pacgan the power of two samples in generative adversarial networks"
    - "segan speech enhancement generative adversarial network"
    - "word translation without parallel data"

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)