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: "ensembles of generative adversarial networks"
  Abstract: Ensembles are a popular way to improve results of discriminative CNNs. The combination of several networks trained starting from different initializations improves results significantly. In this paper we investigate the usage of ensembles of GANs. The specific nature of GANs opens up several new ways to construct ensembles. The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal. As such ensembles of GANs can be constructed based on the same network initialization but just taking models which have different amount of iterations. These so-called self ensembles are much faster to train than traditional ensembles. The second method, called cascade GANs, redirects part of the training data which is badly modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset we show that ensembles of GANs obtain model probability distributions which better model the data distribution. In addition, we show that these improved results can be obtained at little additional computational cost.

Connected through hyperedge HE-1 (induced by source paper "snore gans improving automatic snore sound classification with synthesized data"):
  with members:
    - "generative adversarial networks an overview"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "learning to fuse music genres with generative adversarial dual learning"
    - "learning audio sequence representations for acoustic event classification"
    - "segan speech enhancement generative adversarial network"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "end to end multimodal emotion recognition using deep neural networks"

Connected through hyperedge HE-2 (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"
    - "multi generator generative adversarial nets"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "spatial evolutionary generative adversarial networks"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "evolutionary generative adversarial networks"
    - "towards distributed coevolutionary gans"
    - "lipizzaner a system that scales robust generative adversarial network training"
    - "dual discriminator generative adversarial nets"
    - "dual motion gan for future flow embedded video prediction"
    - "generative adversarial parallelization"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial networks for financial trading strategies fine tuning and combination"):
  with members:
    - "evolutionary generative adversarial networks"
    - "generative adversarial networks an overview"
    - "autoregressive quantile networks for generative modeling"
    - "improved training of wasserstein gans"
    - "stacked generative adversarial networks"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "pros and cons of gan evaluation measures"):
  with members:
    - "decoupled learning for conditional adversarial networks"
    - "cross view image synthesis using conditional gans"
    - "generating adversarial examples with adversarial networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "gang of gans generative adversarial networks with maximum margin ranking"
    - "progressive growing of gans for improved quality stability and variation"
    - "sampling generative networks"

Connected through hyperedge HE-6 (induced by source paper "transferring gans generating images from limited data"):
  with members:
    - "cgans with projection discriminator"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "pros and cons of gan evaluation measures"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "activation maximization generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pose guided person image generation"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "md gan multi discriminator generative adversarial networks for distributed datasets"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "generative adversarial parallelization"
    - "style transfer generative adversarial networks learning to play chess differently"
    - "multi generator generative adversarial nets"
    - "revisiting distributed synchronous sgd"
    - "distributed deep learning on edge devices feasibility via adaptive compression"
    - "invertible conditional gans for image editing"
    - "generative multi adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "towards distributed coevolutionary gans"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "generative adversarial parallelization"
    - "multi generator generative adversarial nets"
    - "es is more than just a traditional finite difference approximator"
    - "improved training of wasserstein gans"
    - "do gans actually learn the distribution an empirical study"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "generative multi 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.CV(Computer Vision and Pattern Recognition)