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 "robust lane marking detection algorithm using drivable area segmentation and extended slt"):
  Abstract: In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a bird's eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset.

Includes members:
  Member "pothole detection based on disparity transformation and road surface modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation")
  Member "real time dense stereo embedded in a uav for road inspection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation")
  Member "key ingredients of self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attacking vision based perception in end to end autonomous driving models")
  Member "road damage detection based on unsupervised disparity map segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation")
  Member "real time subpixel fast bilateral stereo":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "road crack detection using deep convolutional neural network and adaptive thresholding")
  Member "road surface 3d reconstruction based on dense subpixel disparity map estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast depth estimation for view synthesis")
  Member "real time stereo vision based lane detection system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation")
  Member "a novel disparity transformation algorithm for road segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "key ingredients of self driving cars")

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.RO(Robotics)