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: "model based outdoor performance capture"
  Abstract: We propose a new model-based method to accurately reconstruct human performances captured outdoors in a multi-camera setup. Starting from a template of the actor model, we introduce a new unified implicit representation for both, articulated skeleton tracking and nonrigid surface shape refinement. Our method fits the template to unsegmented video frames in two stages - first, the coarse skeletal pose is estimated, and subsequently non-rigid surface shape and body pose are jointly refined. Particularly for surface shape refinement we propose a new combination of 3D Gaussians designed to align the projected model with likely silhouette contours without explicit segmentation or edge detection. We obtain reconstructions of much higher quality in outdoor settings than existing methods, and show that we are on par with state-of-the-art methods on indoor scenes for which they were designed

Connected through hyperedge HE-1 (induced by source paper "eventcap monocular 3d capture of high speed human motions using an event camera"):
  with members:
    - "event based vision a survey"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"
    - "temporal upsampling of depth maps using a hybrid camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "asynchronous photometric feature tracking using events and frames"
    - "bringing a blurry frame alive at high frame rate with an event camera"

Connected through hyperedge HE-2 (induced by source paper "learning to reconstruct people in clothing from a single rgb camera"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "instance level human parsing via part grouping network"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-3 (induced by source paper "detailed human shape estimation from a single image by hierarchical mesh deformation"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "densereg fully convolutional dense shape regression in the wild"
    - "video based reconstruction of 3d people models"
    - "view extrapolation of human body from a single image"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "deep textured 3d reconstruction of human bodies"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "a robust billboard based free viewpoint video synthesizing algorithm for sports scenes"):
  with members:
    - "monocular depth estimation a survey"
    - "deep ordinal regression network for monocular depth estimation"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "soccer on your tabletop"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "rethinking atrous convolution for semantic image segmentation"
    - "convolutional pose machines"

Connected through hyperedge HE-5 (induced by source paper "monocular total capture posing face body and hands in the wild"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "headon real time reenactment of human portrait videos"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "drpose3d depth ranking in 3d human pose estimation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "video based reconstruction of 3d people models"):
  with members:
    - "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"
    - "monoperfcap human performance capture from monocular video"
    - "compositional human pose regression"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "deep multitask architecture for integrated 2d and 3d human sensing"

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"
    - "capturing dynamic textured surfaces of moving targets"
    - "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)