human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "road network reconstruction from satellite images with machine learning supported by topological methods"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "graph reconstruction by discrete morse theory"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "linenet a zoomable cnn for crowdsourced high definition maps modeling in urban environments"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"
    - "zoom better to see clearer human and object parsing with hierarchical auto zoom net"
    - "real time detection of lane markers in urban streets"

Question: Please tell me which class the center node 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)