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 "a methodological review of visual road recognition procedures for autonomous driving applications"):
  Abstract: The current research interest in autonomous driving is growing at a rapid pace, attracting great investments from both the academic and corporate sectors. In order for vehicles to be fully autonomous, it is imperative that the driver assistance system is adapt in road and lane keeping. In this paper, we present a methodological review of techniques with a focus on visual road detection and recognition. We adopt a pragmatic outlook in presenting this review, whereby the procedures of road recognition is emphasised with respect to its practical implementations. The contribution of this review hence covers the topic in two parts -- the first part describes the methodological approach to conventional road detection, which covers the algorithms and approaches involved to classify and segregate roads from non-road regions; and the other part focuses on recent state-of-the-art machine learning techniques that are applied to visual road recognition, with an emphasis on methods that incorporate convolutional neural networks and semantic segmentation. A subsequent overview of recent implementations in the commercial sector is also presented, along with some recent research works pertaining to road detections.

Includes members:
  Member "fast lidar based road detection using fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless compression of deep neural networks")
  Member "enet a deep neural network architecture for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "a review on deep learning techniques applied to semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "learning a driving simulator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a little fog for a large turn")
  Member "multinet real time joint semantic reasoning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised learning for autonomous vehicles perception a conciliation between analytical and learning methods")
  Member "end to end learning for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving 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)