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: "drpose3d depth ranking in 3d human pose estimation"
  Abstract: In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation. Instead of accurate 3D positions, the depth ranking can be identified by human intuitively and learned using the deep neural network more easily by solving classification problems. Moreover, depth ranking contains rich 3D information. It prevents the 2D-to-3D pose regression in two-stage methods from being ill-posed. In our method, firstly, we design a Pairwise Ranking Convolutional Neural Network (PRCNN) to extract depth rankings of human joints from images. Secondly, a coarse-to-fine 3D Pose Network(DPNet) is proposed to estimate 3D poses from both depth rankings and 2D human joint locations. Additionally, to improve the generality of our model, we introduce a statistical method to augment depth rankings. Our approach outperforms the state-of-the-art methods in the Human3.6M benchmark for all three testing protocols, indicating that depth ranking is an essential geometric feature which can be learned to improve the 3D pose estimation.

Connected through hyperedge HE-1 (induced by source paper "unsupervised 3d pose estimation with geometric self supervision"):
  with members:
    - "learning to generate synthetic data via compositing"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised adversarial learning of 3d human pose from 2d joint locations"
    - "3d human pose estimation with relational networks"
    - "3d human pose estimation in the wild by adversarial learning"
    - "can 3d pose be learned from 2d projections alone"
    - "orinet a fully convolutional network for 3d human pose estimation"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"

Connected through hyperedge HE-3 (induced by source paper "multiview consistent semi supervised learning for 3d human pose estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "3d human pose machines with self supervised learning"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "exploiting temporal context for 3d human pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised discovery of geometry aware representation for 3d human pose estimation"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "learning pose specific representations by predicting different views"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-5 (induced by source paper "temporally coherent full 3d mesh human pose recovery from monocular video"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "simulcap single view human performance capture with cloth simulation"
    - "video based reconstruction of 3d people models"
    - "3d human pose estimation in the wild by adversarial learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "detailed human avatars from monocular video"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"

Connected through hyperedge HE-6 (induced by source paper "3d human pose estimation under limited supervision using metric learning"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "exploring disentangled feature representation beyond face identification"
    - "learning monocular 3d human pose estimation from multi view images"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "self supervised learning of pose embeddings from spatiotemporal relations in videos"

Connected through hyperedge HE-7 (induced by source paper "absolute human pose estimation with depth prediction network"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "fbi pose towards bridging the gap between 2d images and 3d human poses using forward or backward information"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"
    - "megadepth learning single view depth prediction from internet photos"
    - "synthetic occlusion augmentation with volumetric heatmaps for the 2018 eccv posetrack challenge on 3d human pose estimation"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "single shot multi person 3d pose estimation from monocular rgb"

Connected through hyperedge HE-8 (induced by source paper "monocular total capture posing face body and hands in the wild"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "headon real time reenactment of human portrait videos"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "orinet a fully convolutional network 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)