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 viewpoint invariant 3d human pose estimation"
  Abstract: We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve this, our discriminative model embeds local regions into a learned viewpoint invariant feature space. Formulated as a multi-task learning problem, our model is able to selectively predict partial poses in the presence of noise and occlusion. Our approach leverages a convolutional and recurrent network architecture with a top-down error feedback mechanism to self-correct previous pose estimates in an end-to-end manner. We evaluate our model on a previously published depth dataset and a newly collected human pose dataset containing 100K annotated depth images from extreme viewpoints. Experiments show that our model achieves competitive performance on frontal views while achieving state-of-the-art performance on alternate viewpoints.

Connected through hyperedge HE-1 (induced by source paper "predicting animation skeletons for 3d articulated models via volumetric nets"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "p2p net bidirectional point displacement net for shape transform"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deepflux for skeletons in the wild"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "hi fi hierarchical feature integration for skeleton detection"
    - "linear span network for object skeleton detection"
    - "amat medial axis transform for natural images"

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 "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-4 (induced by source paper "multi task human analysis in still images 2d 3d pose depth map and multi part segmentation"):
  with members:
    - "multi scale structure aware network for human pose estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "taskonomy disentangling task transfer learning"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "understanding humans in crowded scenes deep nested adversarial learning and a new benchmark for multi human parsing"

Connected through hyperedge HE-5 (induced by source paper "pifpaf composite fields for human pose estimation"):
  with members:
    - "learning to refine human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "densepose dense human pose estimation in the wild"
    - "towards accurate multi person pose estimation in the wild"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "cascaded pyramid network for multi person pose estimation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-6 (induced by source paper "hand pose estimation a survey"):
  with members:
    - "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"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-7 (induced by source paper "part segmentation for highly accurate deformable tracking in occlusions via fully convolutional neural networks"):
  with members:
    - "bodynet volumetric inference of 3d human body shapes"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "densepose dense human pose estimation in the wild"
    - "dynamic high resolution deformable articulated tracking"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "end to end recovery of human shape and pose"
    - "towards accurate multi person pose estimation in the wild"
    - "vnect real time 3d human pose estimation with a single rgb camera"

Connected through hyperedge HE-8 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "learning where to attend like a human driver"
    - "real time human motion capture with multiple depth cameras"
    - "hcp a flexible cnn framework for multi label image classification"

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)