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: "training discriminative models to evaluate generative ones"
  Abstract: Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks (GANs), produce good visual samples of varied categories of images. However, the validation of their quality is still difficult to define and there is no existing agreement on the best evaluation process. This paper aims at making a step toward an objective evaluation process for generative models. It presents a new method to assess a trained generative model by evaluating the test accuracy of a classifier trained with generated data. The test set is composed of real images. Therefore, The classifier accuracy is used as a proxy to evaluate if the generative model fit the true data distribution. By comparing results with different generated datasets we are able to classify and compare generative models. The motivation of this approach is also to evaluate if generative models can help discriminative neural networks to learn, i.e., measure if training on generated data is able to make a model successful at testing on real settings. Our experiments compare different generators from the Variational Auto-Encoders (VAE) and Generative Adversarial Network (GAN) frameworks on MNIST and fashion MNIST datasets. Our results show that none of the generative models is able to replace completely true data to train a discriminative model. But they also show that the initial GAN and WGAN are the best choices to generate on MNIST database (Modified National Institute of Standards and Technology database) and fashion MNIST database.

Connected through hyperedge HE-1 (induced by source paper "mining gold samples for conditional gans"):
  with members:
    - "variational adversarial active learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "high fidelity image generation with fewer labels"
    - "cgans with projection discriminator"
    - "the relativistic discriminator a key element missing from standard gan"
    - "spectral normalization for generative adversarial networks"
    - "not all samples are created equal deep learning with importance sampling"
    - "discriminative active learning"

Connected through hyperedge HE-2 (induced by source paper "classification accuracy score for conditional generative models"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "geometry score a method for comparing generative adversarial networks"
    - "on accurate evaluation of gans for language generation"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-3 (induced by source paper "continual learning for robotics"):
  with members:
    - "s trigger continual state representation learning via self triggered generative replay"
    - "towards non i i d image classification a dataset and baselines"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "roboturk a crowdsourcing platform for robotic skill learning through imitation"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "generative models from the perspective of continual learning"
    - "domain adaptation for visual applications a comprehensive survey"

Connected through hyperedge HE-4 (induced by source paper "marginal replay vs conditional replay for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "incremental classifier learning with generative adversarial networks"
    - "distillation techniques for pseudo rehearsal based incremental learning"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "keep and learn continual learning by constraining the latent space for knowledge preservation in neural networks"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "deep generative dual memory network for continual learning"

Connected through hyperedge HE-5 (induced by source paper "generative models from the perspective of continual learning"):
  with members:
    - "incremental classifier learning with generative adversarial networks"
    - "distillation techniques for pseudo rehearsal based incremental learning"
    - "pros and cons of gan evaluation measures"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "continual learning in generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "a strategy for an uncompromising incremental learner"

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)