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 "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints"):
  Abstract: Creating plausible virtual actors from images of real actors remains one of the key challenges in computer vision and computer graphics. Marker-less human motion estimation and shape modeling from images in the wild bring this challenge to the fore. Although the recent advances on view synthesis and image-to-image translation, currently available formulations are limited to transfer solely style and do not take into account the character's motion and shape, which are by nature intermingled to produce plausible human forms. In this paper, we propose a unifying formulation for transferring appearance and retargeting human motion from monocular videos that regards all these aspects. Our method synthesizes new videos of people in a different context where they were initially recorded. Differently from recent appearance transferring methods, our approach takes into account body shape, appearance, and motion constraints. The evaluation is performed with several experiments using publicly available real videos containing hard conditions. Our method is able to transfer both human motion and appearance outperforming state-of-the-art methods, while preserving specific features of the motion that must be maintained (e.g., feet touching the floor, hands touching a particular object) and holding the best visual quality and appearance metrics such as Structural Similarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS).

Includes members:
  Member "learning character agnostic motion for motion retargeting in 2d":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "learning to reconstruct people in clothing from a single rgb camera":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "pifu pixel aligned implicit function for high resolution clothed human digitization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to transfer texture from clothing images to 3d humans")
  Member "video to video synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "synthesizing images of humans in unseen poses":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "deep video based performance cloning":
    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 "neural kinematic networks for unsupervised motion retargetting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep differentiable grasp planner for high dof grippers")
  Member "video based reconstruction of 3d people models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")

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)