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 "deepfuse an imu aware network for real time 3d human pose estimation from multi view image"):
  Abstract: In this paper, we propose a two-stage fully 3D network, namely \textbf{DeepFuse}, to estimate human pose in 3D space by fusing body-worn Inertial Measurement Unit (IMU) data and multi-view images deeply. The first stage is designed for pure vision estimation. To preserve data primitiveness of multi-view inputs, the vision stage uses multi-channel volume as data representation and 3D soft-argmax as activation layer. The second one is the IMU refinement stage which introduces an IMU-bone layer to fuse the IMU and vision data earlier at data level. without requiring a given skeleton model a priori, we can achieve a mean joint error of $28.9$mm on TotalCapture dataset and $13.4$mm on Human3.6M dataset under protocol 1, improving the SOTA result by a large margin. Finally, we discuss the effectiveness of a fully 3D network for 3D pose estimation experimentally which may benefit future research.

Includes members:
  Member "self supervised learning of 3d human pose using multi view geometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "fast and robust multi person 3d pose estimation from multiple views":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "4d association graph for realtime multi person motion capture using multiple video cameras")
  Member "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross view tracking for multi human 3d pose estimation at over 100 fps")
  Member "a generalizable approach for multi view 3d human pose regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast and robust multi person 3d pose estimation from multiple views")
  Member "multi scale structure aware network for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation")
  Member "deep autoencoder for combined human pose estimation and body model upscaling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial based neural networks for affect estimations in the wild")
  Member "2d 3d pose estimation and action recognition using multitask deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "3d human pose estimation with 2d marginal heatmaps":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "metric scale truncation robust heatmaps for 3d human 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)