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: "mcgan mean and covariance feature matching gan"
  Abstract: We introduce new families of Integral Probability Metrics (IPM) for training Generative Adversarial Networks (GAN). Our IPMs are based on matching statistics of distributions embedded in a finite dimensional feature space. Mean and covariance feature matching IPMs allow for stable training of GANs, which we will call McGan. McGan minimizes a meaningful loss between distributions.

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"
    - "dist gan an improved gan using distance constraints"
    - "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 "microbatchgan stimulating diversity with multi adversarial discrimination"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "on convergence and stability of gans"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "dual discriminator generative adversarial nets"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pacgan the power of two samples in generative adversarial networks"
    - "the numerics of gans"

Connected through hyperedge HE-3 (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"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-4 (induced by source paper "dense 3d object reconstruction from a single depth view"):
  with members:
    - "defo net learning body deformation using generative adversarial networks"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "progressive growing of gans for improved quality stability and variation"
    - "end to end 3d face reconstruction with deep neural networks"
    - "learning a multi view stereo machine"

Connected through hyperedge HE-5 (induced by source paper "learning generative models across incomparable spaces"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "emnist an extension of mnist to handwritten letters"
    - "mmd gan towards deeper understanding of moment matching network"

Connected through hyperedge HE-6 (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"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "which training methods for gans do actually converge"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "learning implicit generative models by matching perceptual features"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning implicit generative models with the method of learned moments"
    - "deep clustering for unsupervised learning of visual features"
    - "spectral normalization for generative adversarial networks"
    - "implicit maximum likelihood estimation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein 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.LG(Machine Learning)