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: "capturing dynamic textured surfaces of moving targets"
  Abstract: We present an end-to-end system for reconstructing complete watertight and textured models of moving subjects such as clothed humans and animals, using only three or four handheld sensors. The heart of our framework is a new pairwise registration algorithm that minimizes, using a particle swarm strategy, an alignment error metric based on mutual visibility and occlusion. We show that this algorithm reliably registers partial scans with as little as 15% overlap without requiring any initial correspondences, and outperforms alternative global registration algorithms. This registration algorithm allows us to reconstruct moving subjects from free-viewpoint video produced by consumer-grade sensors, without extensive sensor calibration, constrained capture volume, expensive arrays of cameras, or templates of the subject geometry.

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 "neural rendering and reenactment of human actor videos"):
  with members:
    - "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"
    - "pose guided person image generation"
    - "progressive growing of gans for improved quality stability and variation"
    - "carla an open urban driving simulator"
    - "a generative model of people in clothing"
    - "deformable gans for pose based human image generation"

Connected through hyperedge HE-3 (induced by source paper "neural rendering and reenactment of human actor videos"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "view extrapolation of human body from a single image"
    - "progressive growing of gans for improved quality stability and variation"
    - "carla an open urban driving simulator"
    - "a generative model of people in clothing"
    - "deformable gans for pose based human image generation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "deepdeform learning non rigid rgb d reconstruction with semi supervised data"):
  with members:
    - "topology aware non rigid point cloud registration"
    - "3d coded 3d correspondences by deep deformation"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "lift learned invariant feature transform"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "computing the stereo matching cost with a convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "visibility constrained generative model for depth based 3d facial pose tracking"):
  with members:
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "3d face morphable models in the wild"
    - "learning detailed face reconstruction from a single image"
    - "dense human body correspondences using convolutional networks"
    - "review of statistical shape spaces for 3d data with comparative analysis for human faces"

Connected through hyperedge HE-6 (induced by source paper "nrmvs non rigid multi view stereo"):
  with members:
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "scalable dense monocular surface reconstruction"

Connected through hyperedge HE-7 (induced by source paper "reticam real time human performance capture from monocular video"):
  with members:
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "densepose dense human pose estimation in the wild"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-8 (induced by source paper "robust 3d human motion reconstruction via dynamic template construction"):
  with members:
    - "temporally coherent 4d reconstruction of complex dynamic scenes"
    - "model based outdoor performance capture"
    - "general automatic human shape and motion capture using volumetric contour cues"
    - "general dynamic scene reconstruction from multiple view video"

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)