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: "evolutionary generative adversarial networks"
  Abstract: Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversarial networks (E-GAN) for stable GAN training and improved generative performance. Unlike existing GANs, which employ a pre-defined adversarial objective function alternately training a generator and a discriminator, we utilize different adversarial training objectives as mutation operations and evolve a population of generators to adapt to the environment (i.e., the discriminator). We also utilize an evaluation mechanism to measure the quality and diversity of generated samples, such that only well-performing generator(s) are preserved and used for further training. In this way, E-GAN overcomes the limitations of an individual adversarial training objective and always preserves the best offspring, contributing to progress in and the success of GANs. Experiments on several datasets demonstrate that E-GAN achieves convincing generative performance and reduces the training problems inherent in existing GANs.

Connected through hyperedge HE-1 (induced by source paper "mdea malware detection with evolutionary adversarial learning"):
  with members:
    - "the advantages of multiple classes for reducing overfitting from test set reuse"
    - "learning to evade static pe machine learning malware models via reinforcement learning"
    - "regularized evolution for image classifier architecture search"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-2 (induced by source paper "stabilizing generative adversarial network training a survey"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "multi objective training of generative adversarial networks with multiple discriminators"
    - "evaluating gans via duality"
    - "convergence problems with generative adversarial networks gans"
    - "the relativistic discriminator a key element missing from standard gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"

Connected through hyperedge HE-3 (induced by source paper "augmenting correlation structures in spatial data using deep generative models"):
  with members:
    - "generative adversarial networks for financial trading strategies fine tuning and combination"
    - "coco gan generation by parts via conditional coordinating"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "point cloud gan"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "catgan category aware generative adversarial networks with hierarchical evolutionary learning for category text generation"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "the relativistic discriminator a key element missing from standard gan"
    - "adversarial text generation via feature mover s distance"
    - "relational recurrent neural networks"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "neural machine translation with gumbel greedy decoding"

Connected through hyperedge HE-5 (induced by source paper "coegan evaluating the coevolution effect in generative adversarial networks"):
  with members:
    - "coevolution of generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "towards distributed coevolutionary gans"
    - "evolving deep neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "distillhash unsupervised deep hashing by distilling data pairs"):
  with members:
    - "fast supervised discrete hashing"
    - "shared predictive cross modal deep quantization"
    - "triplet based deep hashing network for cross modal retrieval"
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "zero shot sketch image hashing"
    - "masking a new perspective of noisy supervision"
    - "self supervised adversarial hashing networks for cross modal retrieval"

Connected through hyperedge HE-7 (induced by source paper "spatial evolutionary generative adversarial networks"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "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"
    - "triangle generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "generative adversarial networks for financial trading strategies fine tuning and combination"):
  with members:
    - "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"
    - "ensembles of 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)