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: "improved generator objectives for gans"
  Abstract: We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an interpretation for the mismatched GAN generator and discriminator objectives often used in practice, and explains the problem of poor sample diversity. We also derive a family of generator objectives that target arbitrary $f$-divergences without minimizing a lower bound, and use them to train generative image models that target either improved sample quality or greater sample diversity.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "expected information maximization using the i projection for mixture density estimation"):
  with members:
    - "adversarial learning of a sampler based on an unnormalized distribution"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "stabilizing generative adversarial network training a survey"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "multi objective training of generative adversarial networks with multiple discriminators"
    - "evaluating gans via duality"
    - "convergence problems with generative adversarial networks gans"
    - "the relativistic discriminator a key element missing from standard gan"
    - "evolutionary generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"

Connected through hyperedge HE-4 (induced by source paper "human annotations improve gan performances"):
  with members:
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "improved training of wasserstein gans"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "on variational bounds of mutual information"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "invariant representations without adversarial training"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "formal limitations on the measurement of mutual information"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-6 (induced by source paper "dgsan discrete generative self adversarial network"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "texygen a benchmarking platform for text generation models"
    - "adversarial ranking for language generation"
    - "long text generation via adversarial training with leaked information"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "mr gan manifold regularized generative adversarial networks"):
  with members:
    - "geometry score a method for comparing generative adversarial networks"
    - "disconnected manifold learning for generative adversarial networks"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "mmgan manifold matching generative adversarial network"
    - "the numerics of gans"
    - "stabilizing training of generative adversarial networks through regularization"

Connected through hyperedge HE-8 (induced by source paper "generative modeling using the sliced wasserstein distance"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "progressive growing of gans for improved quality stability and variation"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein gans"
    - "stacked generative adversarial networks"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"
    - "the radon cumulative distribution transform and its application to image classification"

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)