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: "using a single rgb frame for real time 3d hand pose estimation in the wild"
  Abstract: We present a method for the real-time estimation of the full 3D pose of one or more human hands using a single commodity RGB camera. Recent work in the area has displayed impressive progress using RGBD input. However, since the introduction of RGBD sensors, there has been little progress for the case of monocular color input. We capitalize on the latest advancements of deep learning, combining them with the power of generative hand pose estimation techniques to achieve real-time monocular 3D hand pose estimation in unrestricted scenarios. More specifically, given an RGB image and the relevant camera calibration information, we employ a state-of-the-art detector to localize hands. Given a crop of a hand in the image, we run the pretrained network of OpenPose for hands to estimate the 2D location of hand joints. Finally, non-linear least-squares minimization fits a 3D model of the hand to the estimated 2D joint positions, recovering the 3D hand pose. Extensive experimental results provide comparison to the state of the art as well as qualitative assessment of the method in the wild.

Connected through hyperedge HE-1 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation"):
  with members:
    - "adaptive graphical model network for 2d handpose estimation"
    - "3d hand shape and pose estimation from a single rgb image"
    - "geometric pose affordance 3d human pose with scene constraints"
    - "3d hand shape and pose from images in the wild"
    - "deep high resolution representation learning for human pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "augmented skeleton space transfer for depth based hand pose estimation"

Connected through hyperedge HE-2 (induced by source paper "nonparametric structure regularization machine for 2d hand pose estimation"):
  with members:
    - "adaptive graphical model network for 2d handpose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "multi scale structure aware network for human pose estimation"
    - "exploiting temporal information for 3d human pose estimation"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "learning to estimate 3d hand pose from single rgb images"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-3 (induced by source paper "single network whole body pose estimation"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "recycle gan unsupervised video retargeting"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"

Connected through hyperedge HE-4 (induced by source paper "end to end hand mesh recovery from a monocular rgb image"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "face alignment in full pose range a 3d total solution"
    - "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"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-5 (induced by source paper "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "analysis of hand segmentation in the wild"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-6 (induced by source paper "learning joint reconstruction of hands and manipulated objects"):
  with members:
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "hand pose estimation via latent 2 5d heatmap regression"

Connected through hyperedge HE-7 (induced by source paper "honnotate a method for 3d annotation of hand and objects poses"):
  with members:
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-8 (induced by source paper "3d hand shape and pose estimation from a single rgb image"):
  with members:
    - "3d hand shape and pose from images in the wild"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"

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)