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: "estimation of human body shape and posture under clothing"
  Abstract: Estimating the body shape and posture of a dressed human subject in motion represented as a sequence of (possibly incomplete) 3D meshes is important for virtual change rooms and security. To solve this problem, statistical shape spaces encoding human body shape and posture variations are commonly used to constrain the search space for the shape estimate. In this work, we propose a novel method that uses a posture-invariant shape space to model body shape variation combined with a skeleton-based deformation to model posture variation. Our method can estimate the body shape and posture of both static scans and motion sequences of human body scans with clothing that fits relatively closely to the body. In case of motion sequences, our method takes advantage of motion cues to solve for a single body shape estimate along with a sequence of posture estimates. We apply our approach to both static scans and motion sequences and demonstrate that using our method, higher fitting accuracy is achieved than when using a variant of the popular SCAPE model [2,18] as statistical model.

Connected through hyperedge HE-1 (induced by source paper "dressing 3d humans using a conditional mesh vae gan"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "meshgan non linear 3d morphable models of faces"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"

Connected through hyperedge HE-2 (induced by source paper "shape aware human pose and shape reconstruction using multi view images"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "fashion is taking shape understanding clothing preference based on body shape from online sources"

Connected through hyperedge HE-3 (induced by source paper "towards 3d human shape recovery under clothing"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "monocular total capture posing face body and hands in the wild"
    - "deepwrinkles accurate and realistic clothing modeling"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "a generative model of people in clothing"
    - "unite the people closing the loop between 3d and 2d human representations"

Connected through hyperedge HE-4 (induced by source paper "simulcap single view human performance capture with cloth simulation"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "articulatedfusion real time reconstruction of motion geometry and segmentation using a single depth camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "detailed human avatars from monocular video"
    - "deepwrinkles accurate and realistic clothing modeling"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"

Connected through hyperedge HE-5 (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-6 (induced by source paper "detailed human avatars from monocular video"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "end to end recovery of human shape and pose"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

Connected through hyperedge HE-7 (induced by source paper "detailed accurate human shape estimation from clothed 3d scan sequences"):
  with members:
    - "building statistical shape spaces for 3d human modeling"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "general automatic human shape and motion capture using volumetric contour cues"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

Connected through hyperedge HE-8 (induced by source paper "building statistical shape spaces for 3d human modeling"):
  with members:
    - "estimating 3d human shapes from measurements"

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)