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: "general automatic human shape and motion capture using volumetric contour cues"
  Abstract: Markerless motion capture algorithms require a 3D body with properly personalized skeleton dimension and/or body shape and appearance to successfully track a person. Unfortunately, many tracking methods consider model personalization a different problem and use manual or semi-automatic model initialization, which greatly reduces applicability. In this paper, we propose a fully automatic algorithm that jointly creates a rigged actor model commonly used for animation - skeleton, volumetric shape, appearance, and optionally a body surface - and estimates the actor's motion from multi-view video input only. The approach is rigorously designed to work on footage of general outdoor scenes recorded with very few cameras and without background subtraction. Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy. For reconstruction, 3D body shape is approximated as Gaussian density field. For pose and shape estimation, we minimize a new edge-based alignment energy inspired by volume raycasting in an absorbing medium. We further propose a new statistical human body model that represents the body surface, volumetric Gaussian density, as well as variability in skeleton shape. Given any multi-view sequence, our method jointly optimizes the pose and shape parameters of this model fully automatically in a spatiotemporal way.

Connected through hyperedge HE-1 (induced by source paper "deepcap monocular human performance capture using weak supervision"):
  with members:
    - "tailornet predicting clothing in 3d as a function of human pose shape and garment style"
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "tex2shape detailed full human body geometry from a single image"
    - "learning to dress 3d people in generative clothing"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for active human pose estimation"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "gibson env real world perception for embodied agents"
    - "reinforcement learning of active vision for manipulating objects under occlusions"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end recovery of human shape and pose"
    - "a dataset for developing and benchmarking active vision"

Connected through hyperedge HE-3 (induced by source paper "xnect real time multi person 3d human pose estimation with a single rgb camera"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "expressive body capture 3d hands face and body from a single image"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "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"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-7 (induced by source paper "moulding humans non parametric 3d human shape estimation from single images"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "group normalization"
    - "image based synthesis for deep 3d human pose estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "volumetric performance capture from minimal camera viewpoints"
    - "densepose dense human pose estimation in the wild"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-8 (induced by source paper "a neural network for detailed human depth estimation from a single image"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "bodynet volumetric inference of 3d human body shapes"
    - "deep depth completion of a single rgb d image"
    - "video based reconstruction of 3d people models"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "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)