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: "multi generator generative adversarial nets"
  Abstract: We propose a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapse and delivering state-of-the-art results. A minimax formulation is able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN. Generators create samples that are intended to come from the same distribution as the training data, whilst the discriminator determines whether samples are true data or generated by generators, and the classifier specifies which generator a sample comes from. The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model. We term our method Mixture GAN (MGAN). We develop theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon divergence (JSD) between the mixture of generators' distributions and the empirical data distribution is minimal, whilst the JSD among generators' distributions is maximal, hence effectively avoiding the mode collapse. By utilizing parameter sharing, our proposed model adds minimal computational cost to the standard GAN, and thus can also efficiently scale to large-scale datasets. We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by generators.

Connected through hyperedge HE-1 (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"
    - "mcgan mean and covariance feature matching gan"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "venn gan discovering commonalities and particularities of multiple distributions"):
  with members:
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "class distinct and class mutual image generation with gans"
    - "multi agent diverse generative adversarial networks"
    - "modulating early visual processing by language"
    - "gang of gans generative adversarial networks with maximum margin ranking"
    - "progressive growing of gans for improved quality stability and variation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "virtual conditional generative adversarial networks"):
  with members:
    - "megan mixture of experts of generative adversarial networks for multimodal image generation"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "nips 2016 tutorial generative adversarial networks"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "augmenting physiological time series data a case study for sleep apnea detection"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "bagan data augmentation with balancing gan"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "generative multi adversarial networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "is discriminator a good feature extractor"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "convergence problems with generative adversarial networks gans"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "marta gans unsupervised representation learning for remote sensing image classification"
    - "adversarial multi task learning for text classification"
    - "adversarial feature learning"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "learning to generate the unseen via part synthesis and composition"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "image inpainting for irregular holes using partial convolutions"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "scores shape composition with recursive substructure priors"
    - "compositional gan learning image conditional binary composition"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"

Connected through hyperedge HE-7 (induced by source paper "fictitious gan training gans with historical models"):
  with members:
    - "beyond local nash equilibria for adversarial networks"
    - "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"
    - "dual discriminator generative adversarial nets"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "an online learning approach to generative adversarial networks"
    - "multi view generative adversarial networks"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-8 (induced by source paper "componet learning to generate the unseen by part synthesis and composition"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "compositional gan learning conditional image composition"
    - "scores shape composition with recursive substructure priors"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "began boundary equilibrium generative adversarial networks"

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)