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 boundary extraction from satellite imagery"):
  Abstract: Automated driving is becoming a reality. In this new reality, High Definition (HD) Maps play an important role in path planning and vehicle localization. Lane boundary geometry is one of the key components of an HD Map. Such maps are typically created from ground level LiDAR and imagery data, which, while useful in many ways, have many limitations such as prohibitive cost, infrequent update, traffic occlusions, and incomplete coverage. In this paper, we propose a novel method to automatically extract lane boundaries from satellite imagery using pixel-wise segmentation and machine learning. We then convert these unstructured lines into a structured road model by using a hypothesis linking algorithm, which addresses the aforementioned limitations. We also publish a dataset consisting of satellite imagery and the corresponding lane boundaries for future authors to train, test, and evaluate algorithms.

Includes members:
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "aerial image geolocalization from recognition and matching of roads and intersections":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi temporal aerial image registration using semantic features")
  Member "an empirical evaluation of deep learning on highway driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "pixel wise segmentation of street with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a game based approximate verification of deep neural networks with provable guarantees")
  Member "recurrent convolutional neural networks for scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sos selective objective switch for rapid immunofluorescence whole slide image classification")
  Member "vision based road detection in automotive systems a real time expectation driven approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice")

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)