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: "dynamics transfer gan generating video by transferring arbitrary temporal dynamics from a source video to a single target image"
  Abstract: In this paper, we propose Dynamics Transfer GAN; a new method for generating video sequences based on generative adversarial learning. The spatial constructs of a generated video sequence are acquired from the target image. The dynamics of the generated video sequence are imported from a source video sequence, with arbitrary motion, and imposed onto the target image. To preserve the spatial construct of the target image, the appearance of the source video sequence is suppressed and only the dynamics are obtained before being imposed onto the target image. That is achieved using the proposed appearance suppressed dynamics feature. Moreover, the spatial and temporal consistencies of the generated video sequence are verified via two discriminator networks. One discriminator validates the fidelity of the generated frames appearance, while the other validates the dynamic consistency of the generated video sequence. Experiments have been conducted to verify the quality of the video sequences generated by the proposed method. The results verified that Dynamics Transfer GAN successfully transferred arbitrary dynamics of the source video sequence onto a target image when generating the output video sequence. The experimental results also showed that Dynamics Transfer GAN maintained the spatial constructs (appearance) of the target image while generating spatially and temporally consistent video sequences.

Connected through hyperedge HE-1 (induced by source paper "video to video translation for visual speech synthesis"):
  with members:
    - "video to video synthesis"
    - "artistic style transfer for videos and spherical images"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "multimodal unsupervised image to image translation"
    - "to create what you tell generating videos from captions"
    - "ganimation anatomically aware facial animation from a single image"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "video synthesis of human upper body with realistic face"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "recycle gan unsupervised video retargeting"
    - "generating a fusion image one s identity and another s shape"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "everybody dance now"
    - "learning to generate long term future via hierarchical prediction"
    - "pose guided person image generation"
    - "cyclegan face off"

Connected through hyperedge HE-3 (induced by source paper "everybody dance now"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "synthesizing images of humans in unseen poses"
    - "recycle gan unsupervised video retargeting"
    - "deep video based performance cloning"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "generating a fusion image one s identity and another s shape"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-4 (induced by source paper "time aware and view aware video rendering for unsupervised representation learning"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "adversarial framework for unsupervised learning of motion dynamics in videos"
    - "unsupervised learning of view invariant action representations"
    - "improving video generation for multi functional applications"
    - "decomposing motion and content for natural video sequence prediction"
    - "contextvp fully context aware video prediction"
    - "stochastic variational video prediction"
    - "mocogan decomposing motion and content for video generation"

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)