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 "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution"):
  Abstract: Abstract   Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive. Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities). We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban-objects. We propose an end-to-end trainable model, which uses OpenStreetMap annotations as labels. The model can accommodate a variable number of GSV pictures for the ground-based branch and can also function in the absence of ground pictures at prediction time. We test the effectiveness of our model over the area of Ile-de-France, France, and test its generalization abilities on a set of urban-objects from the city of Nantes, France. Our proposed multimodal Convolutional Neural Network achieves considerably higher accuracies than methods that use a single image modality, making it suitable for automatic landuse map updates. Additionally, our approach could be easily scaled to multiple cities, because it is based on data sources available for many cities worldwide.

Includes members:
  Member "deep multi task learning for a geographically regularized semantic segmentation of aerial images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust object extraction from remote sensing data")
  Member "fine grained land use classification at the city scale using ground level images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatial morphing kernel regression for feature interpolation")
  Member "building instance classification using street view images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bigearthnet dataset with a new class nomenclature for remote sensing image understanding")
  Member "deepglobe 2018 a challenge to parse the earth through satellite images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "large scale land cover classification in gaofen 2 satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "land cover classification with high resolution remote sensing images using transferable deep models")
  Member "toward seamless multiview scene analysis from satellite to street level":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "geocoding of trees from street addresses and street level images")
  Member "a unified model for near and remote sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sideinfnet a deep neural network for semi automatic semantic segmentation with side information")
  Member "dense semantic labeling of subdecimeter resolution images with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")

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)