human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "ordinal depth supervision for 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "3d human pose estimation in the wild by adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "towards 3d human pose estimation in the wild a weakly supervised approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "lifting from the deep convolutional 3d pose estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "a simple yet effective baseline for 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "learning from synthetic humans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "vnect real time 3d human pose estimation with a single rgb camera":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")

Question: Please tell me which class the source paper 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)