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 "detecting lane and road markings at a distance with perspective transformer layers"):
  Abstract: Accurate detection of lane and road markings is a task of great importance for intelligent vehicles. In existing approaches, the detection accuracy often degrades with the increasing distance. This is due to the fact that distant lane and road markings occupy a small number of pixels in the image, and scales of lane and road markings are inconsistent at various distances and perspectives. The Inverse Perspective Mapping (IPM) can be used to eliminate the perspective distortion, but the inherent interpolation can lead to artifacts especially around distant lane and road markings and thus has a negative impact on the accuracy of lane marking detection and segmentation. To solve this problem, we adopt the Encoder-Decoder architecture in Fully Convolutional Networks and leverage the idea of Spatial Transformer Networks to introduce a novel semantic segmentation neural network. This approach decomposes the IPM process into multiple consecutive differentiable homographic transform layers, which are called "Perspective Transformer Layers". Furthermore, the interpolated feature map is refined by subsequent convolutional layers thus reducing the artifacts and improving the accuracy. The effectiveness of the proposed method in lane marking detection is validated on two public datasets: TuSimple and ApolloScape

Includes members:
  Member "learning to cluster for proposal free instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d bird s eye view instance segmentation")
  Member "the right angled perspective improving the understanding of road scenes using boosted inverse perspective mapping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "extending multi object tracking systems to better exploit appearance and 3d information")
  Member "towards end to end lane detection an instance segmentation approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "vh hfcn based parking slot and lane markings segmentation on panoramic surround view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dfnet semantic segmentation on panoramic images with dynamic loss weights and residual fusion block")
  Member "vpgnet vanishing point guided network for lane and road marking detection and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "enet a deep neural network architecture for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "semantic instance segmentation with a discriminative loss function":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "spatial as deep spatial cnn for traffic scene understanding":
    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)