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 "y autoencoders disentangling latent representations via sequential encoding"):
  Abstract: In the last few years there have been important advancements in generative models with the two dominant approaches being Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). However, standard Autoencoders (AEs) and closely related structures have remained popular because they are easy to train and adapt to different tasks. An interesting question is if we can achieve state-of-the-art performance with AEs while retaining their good properties. We propose an answer to this question by introducing a new model called Y-Autoencoder (Y-AE). The structure and training procedure of a Y-AE enclose a representation into an implicit and an explicit part. The implicit part is similar to the output of an autoencoder and the explicit part is strongly correlated with labels in the training set. The two parts are separated in the latent space by splitting the output of the encoder into two paths (forming a Y shape) before decoding and re-encoding. We then impose a number of losses, such as reconstruction loss, and a loss on dependence between the implicit and explicit parts. Additionally, the projection in the explicit manifold is monitored by a predictor, that is embedded in the encoder and trained end-to-end with no adversarial losses. We provide significant experimental results on various domains, such as separation of style and content, image-to-image translation, and inverse graphics.

Includes members:
  Member "disentangling factors of variation with cycle consistent variational auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "dual motion gan for future flow embedded video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "a generative model of people in clothing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "stargan unified generative adversarial networks for multi domain image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "disentangling factors of variation by mixing them":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised robust disentangling of latent characteristics for image synthesis")
  Member "towards diverse and natural image descriptions via a conditional gan":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)