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: "robust estimation of 3d human poses from a single image"
  Abstract: Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose after projection due to the lack of depth information. Moreover, current 2D pose estimators are usually inaccurate which may cause errors in the 3D estimation. We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons. (ii) We enforce limb length constraints to eliminate anthropomorphically implausible skeletons. (iii) We estimate a 3D pose by minimizing the $L_1$-norm error between the projection of the 3D pose and the corresponding 2D detection. The $L_1$-norm loss term is robust to inaccurate 2D joint estimations. We use the alternating direction method (ADM) to solve the optimization problem efficiently. Our approach outperforms the state-of-the-arts on three benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of category specific symmetric 3d keypoints from point sets"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "deep non rigid structure from motion"
    - "pytorch an imperative style high performance deep learning library"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-2 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue"):
  with members:
    - "does computer vision matter for action"
    - "learning the depths of moving people by watching frozen people"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning human optical flow"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised learning of object landmarks through conditional image generation"

Connected through hyperedge HE-3 (induced by source paper "semantic graph convolutional networks for 3d human pose regression"):
  with members:
    - "cartoonish sketch based face editing in videos using identity deformation transfer"
    - "quantized densely connected u nets for efficient landmark localization"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to forecast and refine residual motion for image to video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "cr gan learning complete representations for multi view generation"
    - "graph r cnn for scene graph generation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "monocular 3d human pose estimation by generation and ordinal ranking"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "a variational u net for conditional appearance and shape generation"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"
    - "cross modal deep variational hand pose estimation"
    - "exploiting temporal information for 3d human pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"

Connected through hyperedge HE-5 (induced by source paper "deepfuse an imu aware network for real time 3d human pose estimation from multi view image"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "a generalizable approach for multi view 3d human pose regression"
    - "multi scale structure aware network for human pose estimation"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation with 2d marginal heatmaps"

Connected through hyperedge HE-6 (induced by source paper "generalizing monocular 3d human pose estimation in the wild"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "single view stereo matching"
    - "3d human pose estimation in the wild by adversarial learning"
    - "fbi pose towards bridging the gap between 2d images and 3d human poses using forward or backward information"
    - "exploiting temporal information for 3d human pose estimation"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-7 (induced by source paper "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d human pose estimation in the wild by adversarial learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "orinet a fully convolutional network for 3d human pose estimation"
    - "exploiting temporal information for 3d human pose estimation"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-8 (induced by source paper "deep robust and single shot 3d multi person human pose estimation in complex images"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in the wild by adversarial learning"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "towards accurate multi person pose estimation in the wild"
    - "compositional human pose regression"
    - "a simple yet effective baseline for 3d human pose estimation"

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)