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 "automatic labeled lidar data generation based on precise human model"):
  Abstract: Following improvements in deep neural networks, state-of-the-art networks have been proposed for human recognition using point clouds captured by LiDAR. However, the performance of these networks strongly depends on the training data. An issue with collecting training data is labeling. Labeling by humans is necessary to obtain the ground truth label; however, labeling requires huge costs. Therefore, we propose an automatic labeled data generation pipeline, for which we can change any parameters or data generation environments. Our approach uses a human model named Dhaiba and a background of Miraikan and consequently generated realistic artificial data. We present 500k+ data generated by the proposed pipeline. This paper also describes the specification of the pipeline and data details with evaluations of various approaches.

Includes members:
  Member "rethinking atrous convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "driving in the matrix can virtual worlds replace human generated annotations for real world tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "deeper depth prediction with fully convolutional residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "playing for data ground truth from computer games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "pointnet deep learning on point sets for 3d classification and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)