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 "deep scnn based real time object detection for self driving vehicles using lidar temporal data"):
  Abstract: Real-time accurate detection of three-dimensional (3D) objects is a fundamental necessity for self-driving vehicles. Traditional computer-vision approaches are based on convolutional neural networks (CNN). Although the accuracy of using CNN on the KITTI vision benchmark dataset has resulted in great success, few related studies have examined its energy consumption requirements. Spiking neural networks (SNN) and spiking-CNNs (SCNN) have exhibited lower energy consumption rates than CNN. However, few studies have used SNNs or SCNNs to detect objects. Therefore, we developed a novel data preprocessing layer that translates 3D point-cloud spike times into input and employs SCNN on a YOLOv2 architecture to detect objects via spiking signals. Moreover, we present an estimation method for energy consumption and network sparsity. The results demonstrate that the proposed networks ran with a much higher frame rate of 35.7 fps on an NVIDIA GTX 1080i graphical processing unit. Additionally, the proposed networks with skip connections showed better performance than those without skip connections. Both reached state-of-the-art detection accuracy on the KITTI dataset, and our networks consumed an average (low) energy of 0.585 mJ with a mean sparsity of 56.24%.

Includes members:
  Member "complexer yolo real time 3d object detection and tracking on semantic point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "stdp based spiking deep convolutional neural networks for object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "supervised learning based on temporal coding in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "multi view 3d object detection network for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)