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: "hierarchical video generation from orthogonal information optical flow and texture"
  Abstract: Learning to represent and generate videos from unlabeled data is a very challenging problem. To generate realistic videos, it is important not only to ensure that the appearance of each frame is real, but also to ensure the plausibility of a video motion and consistency of a video appearance in the time direction. The process of video generation should be divided according to these intrinsic difficulties. In this study, we focus on the motion and appearance information as two important orthogonal components of a video, and propose Flow-and-Texture-Generative Adversarial Networks (FTGAN) consisting of FlowGAN and TextureGAN. In order to avoid a huge annotation cost, we have to explore a way to learn from unlabeled data. Thus, we employ optical flow as motion information to generate videos. FlowGAN generates optical flow, which contains only the edge and motion of the videos to be begerated. On the other hand, TextureGAN specializes in giving a texture to optical flow generated by FlowGAN. This hierarchical approach brings more realistic videos with plausible motion and appearance consistency. Our experiments show that our model generates more plausible motion videos and also achieves significantly improved performance for unsupervised action classification in comparison to previous GAN works. In addition, because our model generates videos from two independent information, our model can generate new combinations of motion and attribute that are not seen in training data, such as a video in which a person is doing sit-up in a baseball ground.

Connected through hyperedge HE-1 (induced by source paper "better guider predicts future better difference guided generative adversarial networks"):
  with members:
    - "learning to forecast and refine residual motion for image to video generation"
    - "dual motion gan for future flow embedded video prediction"
    - "contextvp fully context aware video prediction"
    - "hp gan probabilistic 3d human motion prediction via gan"
    - "learning to generate long term future via hierarchical prediction"
    - "improved training of wasserstein gans"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-2 (induced by source paper "long term video generation of multiple futures using human poses"):
  with members:
    - "deep video generation prediction and completion of human action sequences"
    - "stochastic adversarial video prediction"
    - "large scale gan training for high fidelity natural image synthesis"
    - "learning to generate long term future via hierarchical prediction"
    - "skeleton aided articulated motion generation"
    - "attention is all you need"
    - "deep representation learning for human motion prediction and classification"
    - "the pose knows video forecasting by generating pose futures"

Connected through hyperedge HE-3 (induced by source paper "learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "attacking speaker recognition with deep generative models"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "pacgan the power of two samples in generative adversarial networks"
    - "stabilizing adversarial nets with prediction methods"
    - "mocogan decomposing motion and content for video generation"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "video to video synthesis"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "cgans with projection discriminator"
    - "pose guided human video generation"
    - "synthesizing images of humans in unseen poses"
    - "deep video based performance cloning"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "world models"

Connected through hyperedge HE-5 (induced by source paper "tganv2 efficient training of large models for video generation with multiple subsampling layers"):
  with members:
    - "video to video synthesis"
    - "pose guided human video generation"
    - "recycle gan unsupervised video retargeting"
    - "deep video generation prediction and completion of human action sequences"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "stochastic adversarial video prediction"
    - "flow grounded spatial temporal video prediction from still images"

Connected through hyperedge HE-6 (induced by source paper "adversarial framework for unsupervised learning of motion dynamics in videos"):
  with members:
    - "video to video synthesis"
    - "video prediction with appearance and motion conditions"
    - "tracking emerges by colorizing videos"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "data distillation towards omni supervised learning"

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)