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 robust roll angle estimation algorithm based on gradient descent"):
  Abstract: This paper presents a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense disparity map by minimizing a global energy using golden section search algorithm. In this paper, to achieve greater computational efficiency, we utilize gradient descent to optimize the aforementioned global energy. The experimental results illustrate that the proposed roll angle estimation algorithm takes fewer iterations to achieve the same precision as the previous method.

Includes members:
  Member "real time dense stereo embedded in a uav for road inspection":
    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 subpixel fast bilateral stereo":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust lane marking detection algorithm using drivable area segmentation and extended slt")
  Member "road surface 3d reconstruction based on dense subpixel disparity map estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation")
  Member "real time stereo vision for road surface 3 d reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "key ingredients of self driving cars")
  Member "a novel disparity transformation algorithm for road segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "key ingredients of self driving cars")
  Member "multiple lane detection algorithm based on optimised dense disparity map estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust lane marking detection algorithm using drivable area segmentation and extended slt")

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)