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: "end to end global to local cnn learning for hand pose recovery in depth data"
  Abstract: Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets.

Connected through hyperedge HE-1 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "a2j anchor to joint regression network for 3d articulated pose estimation from a single depth image"
    - "model based hand pose estimation for generalized hand shape with appearance normalization"
    - "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"
    - "dynamics based 3d skeletal hand tracking"
    - "depth based 3d hand pose estimation from current achievements to future goals"

Connected through hyperedge HE-2 (induced by source paper "handaugment a simple data augmentation method for depth based 3d hand pose estimation"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "a2j anchor to joint regression network for 3d articulated pose estimation from a single depth image"
    - "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a2j anchor to joint regression network for 3d articulated pose estimation from a single depth image"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "deepprior improving fast and accurate 3d hand pose estimation"

Connected through hyperedge HE-4 (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-5 (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"
    - "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"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"

Connected through hyperedge HE-6 (induced by source paper "capturing hand articulations using recurrent neural network for 3d hand pose estimation"):
  with members:
    - "model based hand pose estimation for generalized hand shape with appearance normalization"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "dynamics based 3d skeletal hand tracking"
    - "depth based 3d hand pose estimation from current achievements to future goals"
    - "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"
    - "model based deep hand pose estimation"

Connected through hyperedge HE-7 (induced by source paper "hmtnet 3d hand pose estimation from single depth image based on hand morphological topology"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "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"
    - "dense 3d regression for hand pose estimation"
    - "training a feedback loop for hand pose estimation"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "herald optimizing heterogeneous dnn accelerators for edge devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ai benchmark running deep neural networks on android smartphones"
    - "understanding reuse performance and hardware cost of dnn dataflows a data centric approach"
    - "learning depth from single images with deep neural network embedding focal length"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "aggregated residual transformations for deep neural networks"
    - "u net convolutional networks for biomedical image 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)