human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generative latent flow a framework for non adversarial image generation"):
  Abstract: Generative Adversarial Networks (GANs) have been shown to outperform non-adversarial generative models in terms of the image generation quality by a large margin. Recently, researchers have looked into improving non-adversarial alternatives that can close the gap of generation quality while avoiding some common issues of GANs, such as unstable training and mode collapse. Examples in this direction include Two-stage VAE and Generative Latent Nearest Neighbors. However, a major drawback of these models is that they are slow to train, and in particular, they require two training stages. To address this, we propose Generative Latent Flow (GLF), which uses an auto-encoder to learn the mapping to and from the latent space, and an invertible flow to map the distribution in the latent space to simple i.i.d noise. The advantages of our method include a simple conceptual framework, single stage training and fast convergence. Quantitatively, the generation quality of our model significantly outperforms that of VAEs, and is competitive with GANs' benchmark on commonly used datasets.

Includes members:
  Member "diagnosing and enhancing vae models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ae ot gan training gans from data specific latent distribution")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "large scale gan training for high fidelity natural image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "non adversarial image synthesis with generative latent nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "style generator inversion for image enhancement and animation")
  Member "implicit maximum likelihood estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning generative models using denoising density estimators")
  Member "style transfer from non parallel text by cross alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "gans trained by a two time scale update rule converge to a nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "adversarial variational bayes unifying variational autoencoders and generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial code learning for image generation")

Question: Please tell me which class the source paper 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)