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: "hybrid one shot 3d hand pose estimation by exploiting uncertainties"
  Abstract: Model-based approaches to 3D hand tracking have been shown to perform well in a wide range of scenarios. However, they require initialisation and cannot recover easily from tracking failures that occur due to fast hand motions. Data-driven approaches, on the other hand, can quickly deliver a solution, but the results often suffer from lower accuracy or missing anatomical validity compared to those obtained from model-based approaches. In this work we propose a hybrid approach for hand pose estimation from a single depth image. First, a learned regressor is employed to deliver multiple initial hypotheses for the 3D position of each hand joint. Subsequently, the kinematic parameters of a 3D hand model are found by deliberately exploiting the inherent uncertainty of the inferred joint proposals. This way, the method provides anatomically valid and accurate solutions without requiring manual initialisation or suffering from track losses. Quantitative results on several standard datasets demonstrate that the proposed method outperforms state-of-the-art representatives of the model-based, data-driven and hybrid paradigms.

Connected through hyperedge HE-1 (induced by source paper "learning pose specific representations by predicting different views"):
  with members:
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "unsupervised learning of depth and ego motion from video"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "towards good practices for deep 3d hand pose estimation"
    - "dynamics based 3d skeletal hand tracking"
    - "representation learning by learning to count"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-2 (induced by source paper "using a single rgb frame for real time 3d hand pose estimation in the wild"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "large scale multiview 3d hand pose dataset"
    - "learning to estimate 3d hand pose from single rgb images"
    - "back to rgb 3d tracking of hands and hand object interactions based on short baseline stereo"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "compositional human pose regression"
    - "towards good practices for deep 3d hand pose estimation"
    - "dynamics based 3d skeletal hand tracking"

Connected through hyperedge HE-3 (induced by source paper "occlusion aware hand pose estimation using hierarchical mixture density network"):
  with members:
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "training a feedback loop for hand pose estimation"
    - "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation"

Connected through hyperedge HE-4 (induced by source paper "simultaneous hand pose and skeleton bone lengths estimation from a single depth image"):
  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"
    - "training a feedback loop for hand pose estimation"
    - "two stream convolutional neural network for accurate rgb d fingertip detection using depth and edge information"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation"

Connected through hyperedge HE-5 (induced by source paper "back to rgb 3d tracking of hands and hand object interactions based on short baseline stereo"):
  with members:
    - "dynamics based 3d skeletal hand tracking"
    - "training a feedback loop for hand pose estimation"
    - "3d hand pose tracking and estimation using stereo matching"
    - "capturing hands in action using discriminative salient points and physics simulation"
    - "real time joint tracking of a hand manipulating an object from rgb d input"
    - "fast and robust hand tracking using detection guided optimization"
    - "real time hand tracking using a sum of anisotropic gaussians model"

Connected through hyperedge HE-6 (induced by source paper "model based deep hand pose estimation"):
  with members:
    - "training a feedback loop for hand pose estimation"
    - "see the difference direct pre image reconstruction and pose estimation by differentiating hog"
    - "depth based hand pose estimation methods data and challenges"
    - "fast and robust hand tracking using detection guided optimization"
    - "hands deep in deep learning for hand pose estimation"
    - "caffe convolutional architecture for fast feature embedding"

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)