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: "towards good practices for deep 3d hand pose estimation"
  Abstract: 3D hand pose estimation from single depth image is an important and challenging problem for human-computer interaction. Recently deep convolutional networks (ConvNet) with sophisticated design have been employed to address it, but the improvement over traditional random forest based methods is not so apparent. To exploit the good practice and promote the performance for hand pose estimation, we propose a tree-structured Region Ensemble Network (REN) for directly 3D coordinate regression. It first partitions the last convolution outputs of ConvNet into several grid regions. The results from separate fully-connected (FC) regressors on each regions are then integrated by another FC layer to perform the estimation. By exploitation of several training strategies including data augmentation and smooth $L_1$ loss, proposed REN can significantly improve the performance of ConvNet to localize hand joints. The experimental results demonstrate that our approach achieves the best performance among state-of-the-art algorithms on three public hand pose datasets. We also experiment our methods on fingertip detection and human pose datasets and obtain state-of-the-art accuracy.

Connected through hyperedge HE-1 (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-2 (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-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"
    - "end to end global to local cnn learning for hand pose recovery in depth data"

Connected through hyperedge HE-4 (induced by source paper "fastv2c handnet fast voxel to coordinate hand pose estimation with 3d convolutional neural networks"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"
    - "single image 3d hand reconstruction with mesh convolutions"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "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"

Connected through hyperedge HE-5 (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"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "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-6 (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"
    - "dynamics based 3d skeletal hand tracking"
    - "representation learning by learning to count"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"

Connected through hyperedge HE-7 (induced by source paper "model based hand pose estimation for generalized hand shape with appearance normalization"):
  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"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "learning to estimate 3d hand pose from single rgb images"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "hand3d hand pose estimation using 3d neural network"
    - "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-8 (induced by source paper "3d hand pose estimation using simulation and partial supervision with a shared latent space"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand 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"
    - "unsupervised image to image translation networks"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "feature mapping for learning fast and accurate 3d pose inference from synthetic images"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"

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)