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: "city scale road extraction from satellite imagery v2 road speeds and travel times"
  Abstract: Automated road network extraction from remote sensing imagery remains a significant challenge despite its importance in a broad array of applications. To this end, we explore road network extraction at scale with inference of semantic features of the graph, identifying speed limits and route travel times for each roadway. We call this approach City-Scale Road Extraction from Satellite Imagery v2 (CRESIv2). Including estimates for travel time permits true optimal routing, not just the shortest geographic distance. We compare SpaceNet labels to OpenStreetMap (OSM) labels, and find that models both trained and tested on SpaceNet labels outperform OSM labels by 60% or greater. For a diverse test set of SpaceNet data and a traditional edge weight of geometric distance, we find an aggregate of 5% improvement over existing methods. We also test our algorithm on Google satellite imagery with OpenStreetMap labels, and find a 23% improvement over previous work. Metric scores decrease by only 4% on large graphs when using travel time rather than geometric distance for edge weights, indicating that optimizing routing for travel time is feasible with this approach.

Connected through hyperedge HE-1 (induced by source paper "road network and travel time extraction from multiple look angles with spacenet data"):
  with members:
    - "spacenet mvoi a multi view overhead imagery dataset"
    - "spacenet a remote sensing dataset and challenge series"
    - "from satellite imagery to disaster insights"
    - "deep learning in agriculture a survey"
    - "benchmark dataset for automatic damaged building detection from post hurricane remotely sensed imagery"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a large dataset of object scans"
    - "u net convolutional networks for biomedical image segmentation"

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)