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: "fbi pose towards bridging the gap between 2d images and 3d human poses using forward or backward information"
  Abstract: Although significant advances have been made in the area of human poses estimation from images using deep Convolutional Neural Network (ConvNet), it remains a big challenge to perform 3D pose inference in-the-wild. This is due to the difficulty to obtain 3D pose groundtruth for outdoor environments. In this paper, we propose a novel framework to tackle this problem by exploiting the information of each bone indicating if it is forward or backward with respect to the view of the camera(we term it Forwardor-Backward Information abbreviated as FBI). Our method firstly trains a ConvNet with two branches which maps an image of a human to both the 2D joint locations and the FBI of bones. These information is further fed into a deep regression network to predict the 3D positions of joints. To support the training, we also develop an annotation user interface and labeled such FBI for around 12K in-the-wild images which are randomly selected from MPII (a public dataset of 2D pose annotation). Our experimental results on the standard benchmarks demonstrate that our approach outperforms state-of-the-art methods both qualitatively and quantitatively.

Connected through hyperedge HE-1 (induced by source paper "hemlets posh learning part centric heatmap triplets for 3d human pose and shape 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"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "denserac joint 3d pose and shape estimation by dense render and compare"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "hemlets pose learning part centric heatmap triplets for accurate 3d human pose estimation"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"

Connected through hyperedge HE-2 (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"
    - "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"
    - "compositional human pose regression"

Connected through hyperedge HE-3 (induced by source paper "consensus based optimization for 3d human pose estimation in camera coordinates"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "how do neural networks see depth in single images"
    - "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 in the wild by adversarial learning"
    - "exploiting temporal information for 3d human pose estimation"
    - "learning monocular 3d human pose estimation from multi view images"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-4 (induced by source paper "hemlets pose learning part centric heatmap triplets for accurate 3d human pose estimation"):
  with members:
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "multi scale structure aware network for human pose estimation"
    - "how robust is 3d human pose estimation to occlusion"
    - "3d human pose estimation in the wild by adversarial learning"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-5 (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"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "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-6 (induced by source paper "3d human pose estimation with siamese equivariant embedding"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"
    - "exploiting temporal information for 3d human pose estimation"
    - "sampling matters in deep embedding learning"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"

Connected through hyperedge HE-7 (induced by source paper "adversarial 3d human pose estimation via multimodal depth supervision"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in the wild by adversarial learning"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "stacked hourglass networks for human pose estimation"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "deepcut joint subset partition and labeling for multi person 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)