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: "hand pose estimation through semi supervised and weakly supervised learning"
  Abstract: We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input. Raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. This intermediate representation contains important topological information and provides useful cues for reasoning about joint locations. The mapping from raw depth to segmentation maps is learned in a semi/weakly-supervised way from two different datasets: (i) a synthetic dataset created through a rendering pipeline including densely labeled ground truth (pixelwise segmentations); and (ii) a dataset with real images for which ground truth joint positions are available, but not dense segmentations. Loss for training on real images is generated from a patch-wise restoration process, which aligns tentative segmentation maps with a large dictionary of synthetic poses. The underlying premise is that the domain shift between synthetic and real data is smaller in the intermediate representation, where labels carry geometric and topological meaning, than in the raw input domain. Experiments on the NYU dataset show that the proposed training method decreases error on joints over direct regression of joints from depth data by 15.7%.

Connected through hyperedge HE-1 (induced by source paper "generalized feedback loop for joint hand object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "dynamics based 3d skeletal hand tracking"

Connected through hyperedge HE-2 (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-3 (induced by source paper "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"):
  with members:
    - "iterative multi domain regularized deep learning for anatomical structure detection and segmentation from ultrasound images"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "weakly supervised discovery of visual pattern configurations"

Connected through hyperedge HE-4 (induced by source paper "murauer mapping unlabeled real data for label austerity"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "learning pose specific representations by predicting different views"
    - "3d hand pose estimation using simulation and partial supervision with a shared latent space"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "cross modal deep variational hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "structure aware 3d hourglass network for hand pose estimation from single depth image"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "dense 3d regression for hand pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "human pose estimation via convolutional part heatmap regression"

Connected through hyperedge HE-6 (induced by source paper "feature mapping for learning fast and accurate 3d pose inference from synthetic images"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "adversarial discriminative domain adaptation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"

Connected through hyperedge HE-7 (induced by source paper "deepprior improving fast and accurate 3d hand pose estimation"):
  with members:
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "dynamics based 3d skeletal hand tracking"
    - "training a feedback loop for hand pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "structured prediction of 3d human pose with deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "handseg an automatically labeled dataset for hand segmentation from depth images"):
  with members:
    - "intel realsense stereoscopic depth cameras"
    - "learning to estimate 3d hand pose from single rgb images"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "dynamics based 3d skeletal hand tracking"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "training deep networks to be spatially sensitive"
    - "mask r cnn"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

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)