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 "lane detection and classification using cascaded cnns"):
  Abstract: Lane detection is extremely important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. As many other computer vision based tasks, convolutional neural networks (CNNs) represent the state-of-the-art technology to indentify lane boundaries. However, the position of the lane boundaries w.r.t. the vehicle may not suffice for a reliable positioning, as for path planning or localization information regarding lane types may also be needed. In this work, we present an end-to-end system for lane boundary identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. To build the system, 14336 lane boundaries instances of the TuSimple dataset for lane detection have been labelled using 8 different classes. Our dataset and the code for inference are available online.

Includes members:
  Member "end to end lane detection through differentiable least squares fitting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "deep multi sensor lane detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "learning to cluster for proposal free instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detecting lane and road markings at a distance with perspective transformer layers")
  Member "towards end to end lane detection an instance segmentation approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "efficient road lane marking detection with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advances in centerline estimation for autonomous lateral control")
  Member "el gan embedding loss driven generative adversarial networks for lane detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations")
  Member "fast lidar based road detection using fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "vpgnet vanishing point guided network for lane and road marking detection and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semi local 3d lane detection and uncertainty 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)