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 "gac gan a general method for appearance controllable human video motion transfer"):
  Abstract: Human video motion transfer has a wide range of applications in multimedia, computer vision and graphics. Recently, due to the rapid development of Generative Adversarial Networks (GANs), there has been significant progress in the field. However, almost all existing GAN-based works are prone to address the mapping from human motions to video scenes, with scene appearances are encoded individually in the trained models. Therefore, each trained model can only generate videos with a specific scene appearance, new models are required to be trained to generate new appearances. Besides, existing works lack the capability of appearance control. For example, users have to provide video records of wearing new clothes or performing in new backgrounds to enable clothes or background changing in their synthetic videos, which greatly limits the application flexibility. In this paper, we propose GAC-GAN, a general method for appearance-controllable human video motion transfer. To enable general-purpose appearance synthesis, we propose to include appearance information in the conditioning inputs. Thus, once trained, our model can generate new appearances by altering the input appearance information. To achieve appearance control, we first obtain the appearance-controllable conditioning inputs and then utilize a two-stage GAC-GAN to generate the corresponding appearance-controllable outputs, where we utilize an ACGAN loss and a shadow extraction module for output foreground and background appearance control respectively. We further build a solo dance dataset containing a large number of dance videos for training and evaluation. Experimental results show that, our proposed GAC-GAN can not only support appearance-controllable human video motion transfer but also achieve higher video quality than state-of-art methods.

Includes members:
  Member "neural rendering and reenactment of human actor videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "example guided style consistent image synthesis from semantic labeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "dance dance generation motion transfer for internet videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot video to video synthesis")
  Member "cgans with projection discriminator":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object centric image generation from layouts")
  Member "soft gated warping gan for pose guided person image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "synthesizing images of humans in unseen poses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "look into person joint body parsing pose estimation network and a new benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep portrait image completion and extrapolation")
  Member "deep video based performance cloning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")

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)