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: "neural rendering and reenactment of human actor videos"
  Abstract: We propose a method for generating video-realistic animations of real humans under user control. In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic three-dimensional (3D) model of the human but instead rely on a video sequence in conjunction with a (medium-quality) controllable 3D template model of the person. With that, our approach significantly reduces production cost compared to conventional rendering approaches based on production-quality 3D models and can also be used to realistically edit existing videos. Technically, this is achieved by training a neural network that translates simple synthetic images of a human character into realistic imagery. For training our networks, we first track the 3D motion of the person in the video using the template model and subsequently generate a synthetically rendered version of the video. These images are then used to train a conditional generative adversarial network that translates synthetic images of the 3D model into realistic imagery of the human. We evaluate our method for the reenactment of another person that is tracked to obtain the motion data, and show video results generated from artist-designed skeleton motion. Our results outperform the state of the art in learning-based human image synthesis.

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"
    - "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"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-2 (induced by source paper "neural voxel renderer learning an accurate and controllable rendering tool"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "texture fields learning texture representations in function space"

Connected through hyperedge HE-3 (induced by source paper "textured neural avatars"):
  with members:
    - "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"
    - "deep video based performance cloning"
    - "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 "a novel bilevel paradigm for image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "few shot segmentation propagation with guided networks"
    - "every smile is unique landmark guided diverse smile generation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"):
  with members:
    - "video to video synthesis"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "view extrapolation of human body from a single image"
    - "a variational u net for conditional appearance and shape generation"
    - "dense pose transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-6 (induced by source paper "gac gan a general method for appearance controllable human video motion transfer"):
  with members:
    - "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"
    - "deep video based performance cloning"
    - "pcgan partition controlled human image generation"

Connected through hyperedge HE-7 (induced by source paper "metapix few shot video retargeting"):
  with members:
    - "dance dance generation motion transfer for internet videos"
    - "few shot adversarial learning of realistic neural talking head models"
    - "video to video synthesis"
    - "on first order meta learning algorithms"
    - "synthesizing images of humans in unseen poses"
    - "recycle gan unsupervised video retargeting"
    - "dense pose transfer"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "text based editing of talking head video"):
  with members:
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "visemenet audio driven animator centric speech animation"
    - "unsupervised training for 3d morphable model regression"
    - "a hybrid model for identity obfuscation by face replacement"
    - "everybody dance now"
    - "progressive growing of gans for improved quality stability and variation"

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)