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: "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation"
  Abstract: Discriminative methods often generate hand poses kinematically implausible, then generative methods are used to correct (or verify) these results in a hybrid method. Estimating 3D hand pose in a hierarchy, where the high-dimensional output space is decomposed into smaller ones, has been shown effective. Existing hierarchical methods mainly focus on the decomposition of the output space while the input space remains almost the same along the hierarchy. In this paper, a hybrid hand pose estimation method is proposed by applying the kinematic hierarchy strategy to the input space (as well as the output space) of the discriminative method by a spatial attention mechanism and to the optimization of the generative method by hierarchical Particle Swarm Optimization (PSO). The spatial attention mechanism integrates cascaded and hierarchical regression into a CNN framework by transforming both the input(and feature space) and the output space, which greatly reduces the viewpoint and articulation variations. Between the levels in the hierarchy, the hierarchical PSO forces the kinematic constraints to the results of the CNNs. The experimental results show that our method significantly outperforms four state-of-the-art methods and three baselines on three public benchmarks.

Connected through hyperedge HE-1 (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-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 "silhouette net 3d hand pose estimation from silhouettes"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "hand3d hand pose estimation using 3d neural network"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "towards good practices for deep 3d hand pose estimation"

Connected through hyperedge HE-4 (induced by source paper "pose guided structured region ensemble network for cascaded hand pose estimation"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "intel realsense stereoscopic depth cameras"
    - "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"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "mask r cnn"
    - "training a feedback loop for hand pose estimation"

Connected through hyperedge HE-5 (induced by source paper "towards markerless grasp capture"):
  with members:
    - "contactdb analyzing and predicting grasp contact via thermal imaging"
    - "contactgrasp functional multi finger grasp synthesis from contact"
    - "learning joint reconstruction of hands and manipulated objects"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "capturing hands in action using discriminative salient points and physics simulation"

Connected through hyperedge HE-6 (induced by source paper "monocular total capture posing face body and hands in the wild"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "headon real time reenactment of human portrait videos"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "drpose3d depth ranking in 3d human pose estimation"

Connected through hyperedge HE-7 (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-8 (induced by source paper "augmented skeleton space transfer for depth based hand pose estimation"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "surfnet generating 3d shape surfaces using deep residual networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dynamics based 3d skeletal hand tracking"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"

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)