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: "3d human body reconstruction from a single image via volumetric regression"
  Abstract: This paper proposes the use of an end-to-end Convolutional Neural Network for direct reconstruction of the 3D geometry of humans via volumetric regression. The proposed method does not require the fitting of a shape model and can be trained to work from a variety of input types, whether it be landmarks, images or segmentation masks. Additionally, non-visible parts, either self-occluded or otherwise, are still reconstructed, which is not the case with depth map regression. We present results that show that our method can handle both pose variation and detailed reconstruction given appropriate datasets for training.

Connected through hyperedge HE-1 (induced by source paper "peelnet textured 3d reconstruction of human body using single view rgb image"):
  with members:
    - "humanmeshnet polygonal mesh recovery of humans"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "graphonomy universal human parsing via graph transfer learning"
    - "detailed human shape estimation from a single image by hierarchical mesh deformation"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "layer structured 3d scene inference via view synthesis"
    - "siclope silhouette based clothed people"

Connected through hyperedge HE-2 (induced by source paper "learning 3d human shape and pose from dense body parts"):
  with members:
    - "human mesh recovery from monocular images via a skeleton disentangled representation"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "deephuman 3d human reconstruction from a single image"
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "denserac joint 3d pose and shape estimation by dense render and compare"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "semantic graph convolutional networks for 3d human pose regression"

Connected through hyperedge HE-3 (induced by source paper "pifu pixel aligned implicit function for high resolution clothed human digitization"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "learning category specific mesh reconstruction from image collections"
    - "group normalization"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-4 (induced by source paper "convolutional mesh regression for single image human shape reconstruction"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "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"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-5 (induced by source paper "tex2shape detailed full human body geometry from a single image"):
  with members:
    - "relighting humans occlusion aware inverse rendering for full body human images"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "deephuman 3d human reconstruction from a single image"
    - "simulcap single view human performance capture with cloth simulation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "fml face model learning from videos"

Connected through hyperedge HE-6 (induced by source paper "densebody directly regressing dense 3d human pose and shape from a single color image"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "3d human pose estimation in the wild by adversarial learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "dense pose transfer"

Connected through hyperedge HE-7 (induced by source paper "deephuman 3d human reconstruction from a single image"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "dense 3d object reconstruction from a single depth view"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "bodynet volumetric inference of 3d human body shapes"
    - "densereg fully convolutional dense shape regression in the wild"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "video based reconstruction of 3d people models"
    - "3d human pose estimation in the wild by adversarial learning"

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)