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: "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
  Abstract: Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly non-linear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time. Our system outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge. The code is available in this https URL.

Connected through hyperedge HE-1 (induced by source paper "efficient convolutional neural networks for depth based multi person pose estimation"):
  with members:
    - "knowledge transfer with jacobian matching"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "real time convolutional networks for depth based human pose estimation"
    - "densepose dense human pose estimation in the wild"
    - "domain adaptation for visual applications a comprehensive survey"
    - "an analysis of deep neural network models for practical applications"
    - "cycada cycle consistent adversarial domain adaptation"
    - "lstm pose machines"

Connected through hyperedge HE-2 (induced by source paper "an end to end framework for unconstrained monocular 3d hand pose estimation"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "learning joint reconstruction of hands and manipulated objects"
    - "3d hand shape and pose from images in the wild"
    - "single image 3d hand reconstruction with mesh convolutions"
    - "analysis of hand segmentation in the wild"
    - "cross modal deep variational 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-3 (induced by source paper "deepsketchhair deep sketch based 3d hair modeling"):
  with members:
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "improved training of wasserstein gans"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "photographic image synthesis with cascaded refinement networks"
    - "3d sketching using multi view deep volumetric prediction"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "pixel wise regression 3d hand pose estimation via spatial form representation and differentiable decoder"):
  with members:
    - "pose guided structured region ensemble network for cascaded 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"
    - "depth based 3d hand pose estimation from current achievements to future goals"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-5 (induced by source paper "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"):
  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"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "hand3d hand pose estimation using 3d neural network"
    - "simultaneous hand pose and skeleton bone lengths estimation from a single depth image"
    - "region ensemble network improving convolutional network for hand pose estimation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "context aware deep spatio temporal network for hand pose estimation from depth images"):
  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"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "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"
    - "hand3d hand pose estimation using 3d neural network"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"

Connected through hyperedge HE-8 (induced by source paper "point to pose voting based hand pose estimation using residual permutation equivariant layer"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "triplet center loss for multi view 3d object retrieval"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "hand3d hand pose estimation using 3d neural network"

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)