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: "deep video generation prediction and completion of human action sequences"
  Abstract: Current video generation/prediction/completion results are limited, due to the severe ill-posedness inherent in these three problems. In this paper, we focus on human action videos, and propose a general, two-stage deep framework to generate human action videos with no constraints or arbitrary number of constraints, which uniformly addresses the three problems: video generation given no input frames, video prediction given the first few frames, and video completion given the first and last frames. To solve video generation from scratch, we build a two-stage framework where we first train a deep generative model that generates human pose sequences from random noise, and then train a skeleton-to-image network to synthesize human action videos given the human pose sequences generated. To solve video prediction and completion, we exploit our trained model and conduct optimization over the latent space to generate videos that best suit the given input frame constraints. With our novel method, we sidestep the original ill-posed problems and produce for the first time high-quality video generation/prediction/completion results of much longer duration. We present quantitative and qualitative evaluations to show that our approach outperforms state-of-the-art methods in all three tasks.

Connected through hyperedge HE-1 (induced by source paper "when relation networks meet gans relation gans with triplet loss"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "improving generalization and stability of generative adversarial networks"
    - "gan qp a novel gan framework without gradient vanishing and lipschitz constraint"
    - "the relativistic discriminator a key element missing from standard gan"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-2 (induced by source paper "adversarial generation of informative trajectories for dynamics system identification"):
  with members:
    - "online simultaneous semi parametric dynamics model learning"
    - "full scale continuous synthetic sonar data generation with markov conditional generative adversarial networks"
    - "video to video synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generative adversarial imitation learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction"):
  with members:
    - "frequency domain transformer networks for video prediction"
    - "human action recognition and prediction a survey"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic adversarial video prediction"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "timeception for complex action recognition"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "learning to decompose and disentangle representations for video prediction"

Connected through hyperedge HE-4 (induced by source paper "music oriented dance video synthesis with pose perceptual loss"):
  with members:
    - "dance dance generation motion transfer for internet videos"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-5 (induced by source paper "unsupervised keypoint learning for guiding class conditional video prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "stochastic adversarial video prediction"
    - "flow grounded spatial temporal video prediction from still images"
    - "unsupervised discovery of object landmarks as structural representations"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"

Connected through hyperedge HE-6 (induced by source paper "learning diverse stochastic human action generators by learning smooth latent transitions"):
  with members:
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "human action generation with generative adversarial networks"
    - "stochastic adversarial video prediction"
    - "recurrent transition networks for character locomotion"
    - "combining recurrent neural networks and adversarial training for human motion synthesis and control"
    - "dual motion gan for future flow embedded video prediction"
    - "hp gan probabilistic 3d human motion prediction via gan"

Connected through hyperedge HE-7 (induced by source paper "long term video generation of multiple futures using human poses"):
  with members:
    - "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"
    - "hierarchical video generation from orthogonal information optical flow and texture"
    - "deep representation learning for human motion prediction and classification"
    - "the pose knows video forecasting by generating pose futures"

Connected through hyperedge HE-8 (induced by source paper "step spatial temporal graph convolutional networks for emotion perception from gaits"):
  with members:
    - "identifying emotions from walking using affective and deep features"
    - "video to video synthesis"
    - "pose guided human video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "mocogan decomposing motion and content for video generation"
    - "detect and track efficient pose estimation in videos"

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)