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 based performance cloning"
  Abstract: We present a new video-based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances. All of the training data and the driving performances are provided as ordinary video segments, without motion capture or depth information. Our generative model is realized as a deep neural network with two branches, both of which train the same space-time conditional generator, using shared weights. One branch, responsible for learning to generate the appearance of the target actor in various poses, uses \emph{paired} training data, self-generated from the reference video. The second branch uses unpaired data to improve generation of temporally coherent video renditions of unseen pose sequences. We demonstrate a variety of promising results, where our method is able to generate temporally coherent videos, for challenging scenarios where the reference and driving videos consist of very different dance performances. Supplementary video: this https URL.

Connected through hyperedge HE-1 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "textured neural avatars"
    - "neural rendering and reenactment of human actor videos"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "dense intrinsic appearance flow for human pose transfer"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "synthesizing images of humans in unseen poses"

Connected through hyperedge HE-2 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "video based reconstruction of 3d people models"
    - "sfv reinforcement learning of physical skills from videos"

Connected through hyperedge HE-3 (induced by source paper "textured neural avatars"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "video to video synthesis"
    - "deep video portraits"
    - "synthesizing images of humans in unseen poses"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "densereg fully convolutional dense shape regression in the wild"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-4 (induced by source paper "gac gan a general method for appearance controllable human video motion transfer"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "example guided style consistent image synthesis from semantic labeling"
    - "dance dance generation motion transfer for internet videos"
    - "cgans with projection discriminator"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "pcgan partition controlled human image generation"

Connected through hyperedge HE-5 (induced by source paper "learning character agnostic motion for motion retargeting in 2d"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "on the convergence of adam and beyond"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "sfv reinforcement learning of physical skills from videos"
    - "everybody dance now"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "end to end recovery of human shape and pose"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-6 (induced by source paper "appearance composing gan a general method for appearance controllable human video motion transfer"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "example guided style consistent image synthesis from semantic labeling"
    - "dance dance generation motion transfer for internet videos"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "instance level human parsing via part grouping network"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-7 (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"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "world models"
    - "stochastic adversarial video prediction"

Connected through hyperedge HE-8 (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"
    - "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"
    - "densepose dense human pose estimation in the wild"

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)