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: "domain transfer for 3d pose estimation from color images without manual annotations"
  Abstract: We introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this problem by learning a mapping from paired color and depth images captured with an RGB-D camera. We jointly learn the pose from synthetic depth images that are easy to generate, and learn to align these synthetic depth images with the real depth images. We show our approach for the task of 3D hand pose estimation and 3D object pose estimation, both from color images only. Our method achieves performances comparable to state-of-the-art methods on popular benchmark datasets, without requiring any annotations for the color images.

Connected through hyperedge HE-1 (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"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "monocular total capture posing face body and hands in the wild"

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

Connected through hyperedge HE-3 (induced by source paper "cornet generic 3d corners for 6d pose estimation of new objects without retraining"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"
    - "when regression meets manifold learning for object recognition and pose estimation"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "unsupervised domain adaptation with adversarial residual transform networks"
    - "3d object instance recognition and pose estimation using triplet loss with dynamic margin"
    - "ipose instance aware 6d pose estimation of partly occluded objects"

Connected through hyperedge HE-4 (induced by source paper "adaptive wasserstein hourglass for weakly supervised hand pose estimation from monocular rgb"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "3d human pose estimation in the wild by adversarial learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "large scale multiview 3d hand pose dataset"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-5 (induced by source paper "learning local rgb to cad correspondences for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"

Connected through hyperedge HE-6 (induced by source paper "rgb based 3d hand pose estimation via privileged learning with depth images"):
  with members:
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "training group orthogonal neural networks with privileged information"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"

Connected through hyperedge HE-7 (induced by source paper "matching rgb images to cad models for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"
    - "zero shot deep domain adaptation"
    - "real time seamless single shot 6d object pose prediction"
    - "self supervised intrinsic image decomposition"

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)