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: "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
  Abstract: Articulated hand pose and shape estimation is an important problem for vision-based applications such as augmented reality and animation. In contrast to the existing methods which optimize only for joint positions, we propose a fully supervised deep network which learns to jointly estimate a full 3D hand mesh representation and pose from a single depth image. To this end, a CNN architecture is employed to estimate parametric representations i.e. hand pose, bone scales and complex shape parameters. Then, a novel hand pose and shape layer, embedded inside our deep framework, produces 3D joint positions and hand mesh. Lack of sufficient training data with varying hand shapes limits the generalized performance of learning based methods. Also, manually annotating real data is suboptimal. Therefore, we present SynHand5M: a million-scale synthetic dataset with accurate joint annotations, segmentation masks and mesh files of depth maps. Among model based learning (hybrid) methods, we show improved results on our dataset and two of the public benchmarks i.e. NYU and ICVL. Also, by employing a joint training strategy with real and synthetic data, we recover 3D hand mesh and pose from real images in 3.7ms.

Connected through hyperedge HE-1 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"
    - "single image 3d hand reconstruction with mesh convolutions"
    - "deepgcns can gcns go as deep as cnns"
    - "disentangling latent hands for image synthesis and pose estimation"
    - "3d human pose estimation in the wild by adversarial learning"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "dual grid net hand mesh vertex regression from single depth maps"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "end to end hand mesh recovery from a monocular rgb image"
    - "3d hand shape and pose from images in the wild"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning pose specific representations by predicting different views"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-3 (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"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-4 (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"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-5 (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"
    - "hand pose estimation via latent 2 5d heatmap regression"

Connected through hyperedge HE-6 (induced by source paper "structure from articulated motion accurate and stable monocular 3d reconstruction without training data"):
  with members:
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-7 (induced by source paper "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "exploiting temporal information for 3d human pose estimation"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "end to end recovery of human shape and pose"
    - "simultaneous hand pose and skeleton bone lengths estimation from a single depth image"

Connected through hyperedge HE-8 (induced by source paper "humanmeshnet polygonal mesh recovery of humans"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "3d hand shape and pose from images in the wild"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "monocular total capture posing face body and hands in the wild"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "how powerful are graph neural networks"

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)