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: "f gans in an information geometric nutshell"
  Abstract: Nowozin \textit{et al} showed last year how to extend the GAN \textit{principle} to all $f$-divergences. The approach is elegant but falls short of a full description of the supervised game, and says little about the key player, the generator: for example, what does the generator actually converge to if solving the GAN game means convergence in some space of parameters? How does that provide hints on the generator's design and compare to the flourishing but almost exclusively experimental literature on the subject? #R##N#In this paper, we unveil a broad class of distributions for which such convergence happens --- namely, deformed exponential families, a wide superset of exponential families --- and show tight connections with the three other key GAN parameters: loss, game and architecture. In particular, we show that current deep architectures are able to factorize a very large number of such densities using an especially compact design, hence displaying the power of deep architectures and their concinnity in the $f$-GAN game. This result holds given a sufficient condition on \textit{activation functions} --- which turns out to be satisfied by popular choices. The key to our results is a variational generalization of an old theorem that relates the KL divergence between regular exponential families and divergences between their natural parameters. We complete this picture with additional results and experimental insights on how these results may be used to ground further improvements of GAN architectures, via (i) a principled design of the activation functions in the generator and (ii) an explicit integration of proper composite losses' link function in the discriminator.

Connected through hyperedge HE-1 (induced by source paper "connecting gans and mfgs"):
  with members:
    - "smoothness and stability in gans"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "improving gans using optimal transport"
    - "improved training of wasserstein gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "semantic segmentation using adversarial networks"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-2 (induced by source paper "improving the improved training of wasserstein gans a consistency term and its dual effect"):
  with members:
    - "on convergence and stability of gans"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "triple generative adversarial nets"
    - "calibrating energy based generative adversarial networks"
    - "mmd gan towards deeper understanding of moment matching network"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "a convex duality framework for gans"):
  with members:
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"
    - "solving approximate wasserstein gans to stationarity"
    - "a classification based study of covariate shift in gan distributions"
    - "the numerics of gans"
    - "approximation and convergence properties of generative adversarial learning"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "the inductive bias of restricted f gans"):
  with members:
    - "convergence problems with generative adversarial networks gans"
    - "approximability of discriminators implies diversity in gans"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "the numerics of gans"
    - "approximation and convergence properties of generative adversarial learning"
    - "gradient descent gan optimization is locally stable"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "tighter variational representations of f divergences via restriction to probability measures"

Connected through hyperedge HE-5 (induced by source paper "boosted density estimation remastered"):
  with members:
    - "boosting variational inference an optimization perspective"
    - "boosted generative models"

Connected through hyperedge HE-6 (induced by source paper "pacgan the power of two samples in generative adversarial networks"):
  with members:
    - "on gans and gmms"
    - "spectral normalization for generative adversarial networks"
    - "deep sets"
    - "the inductive bias of restricted f gans"
    - "dual discriminator generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "a classification based study of covariate shift in gan distributions"

Connected through hyperedge HE-7 (induced by source paper "on the discrimination generalization tradeoff in gans"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "the numerics of gans"
    - "generative adversarial mapping networks"
    - "approximation and convergence properties of generative adversarial learning"
    - "a geometric view of optimal transportation and generative model"
    - "on unifying deep generative models"

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)