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: "on gans and gmms"
  Abstract: A longstanding problem in machine learning is to find unsupervised methods that can learn the statistical structure of high dimensional signals. In recent years, GANs have gained much attention as a possible solution to the problem, and in particular have shown the ability to generate remarkably realistic high resolution sampled images. At the same time, many authors have pointed out that GANs may fail to model the full distribution ("mode collapse") and that using the learned models for anything other than generating samples may be very difficult. In this paper, we examine the utility of GANs in learning statistical models of images by comparing them to perhaps the simplest statistical model, the Gaussian Mixture Model. First, we present a simple method to evaluate generative models based on relative proportions of samples that fall into predetermined bins. Unlike previous automatic methods for evaluating models, our method does not rely on an additional neural network nor does it require approximating intractable computations. Second, we compare the performance of GANs to GMMs trained on the same datasets. While GMMs have previously been shown to be successful in modeling small patches of images, we show how to train them on full sized images despite the high dimensionality. Our results show that GMMs can generate realistic samples (although less sharp than those of GANs) but also capture the full distribution, which GANs fail to do. Furthermore, GMMs allow efficient inference and explicit representation of the underlying statistical structure. Finally, we discuss how GMMs can be used to generate sharp images.

Connected through hyperedge HE-1 (induced by source paper "hierarchical modes exploring in generative adversarial networks"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "stochastic adversarial video prediction"
    - "on line adaptative curriculum learning for gans"
    - "visual data synthesis via gan for zero shot video classification"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "semi supervised fusedgan for conditional image generation"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-2 (induced by source paper "setgan improving the stability and diversity of generative models through a permutation invariant architecture"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "pacgan the power of two samples in generative adversarial networks"
    - "do gans actually learn the distribution an empirical study"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "mode seeking generative adversarial networks for diverse image synthesis"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "drit diverse image to image translation via disentangled representations"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "orthogonal wasserstein gans"):
  with members:
    - "sorting out lipschitz function approximation"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "banach wasserstein gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"

Connected through hyperedge HE-5 (induced by source paper "a bayes optimal view on adversarial examples"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "theoretically principled trade off between robustness and accuracy"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "on evaluating adversarial robustness"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarially robust generalization requires more data"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "a rad approach to deep mixture models"):
  with members:
    - "mixture density generative adversarial networks"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "tackling over pruning in variational autoencoders"
    - "neural discrete representation learning"
    - "density estimation using real nvp"

Connected through hyperedge HE-7 (induced by source paper "graph based discriminators sample complexity and expressiveness"):
  with members:
    - "passing tests without memorizing two models for fooling discriminators"
    - "pacgan the power of two samples in generative adversarial networks"
    - "do gans actually learn the distribution an empirical study"
    - "2 notes on classes with vapnik chervonenkis dimension 1"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-8 (induced by source paper "fmri data augmentation via synthesis"):
  with members:
    - "improved training of wasserstein gans"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "conditional generative adversarial nets"

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)