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 "google street view and deep learning a new ground truthing approach for crop mapping"):
  Abstract: Ground referencing is essential for supervised crop mapping. However, conventional ground truthing involves extensive field surveys and post processing, which is costly in terms of time and labor. In this study, we applied a convolutional neural network (CNN) model to explore the efficacy of automatic ground truthing via Google street view (GSV) images in two distinct farming regions: central Illinois and southern California. We demonstrated the feasibility and reliability of the new ground referencing technique further by performing pixel-based crop mapping with vegetation indices as the model input. The results were evaluated using the United States Department of Agriculture (USDA) crop data layer (CDL) products. From 8,514 GSV images, the CNN model screened out 2,645 target crop images. These images were well classified into crop types, including alfalfa, almond, corn, cotton, grape, soybean, and pistachio. The overall GSV image classification accuracy reached 93% in California and 97% in Illinois. We then shifted the image geographic coordinates using fixed empirical coefficients to produce 8,173 crop reference points including 1,764 in Illinois and 6,409 in California. Evaluation of these new reference points with CDL products showed satisfactory coherence, with 94 to 97% agreement. CNN-based mapping also captured the general pattern of crop type distributions. The overall differences between CDL products and our mapping results were 4% in California and 5% in Illinois. Thus, using these deep learning and GSV image techniques, we have provided an efficient and cost-effective alternative method for ground referencing and crop mapping.

Includes members:
  Member "segnet a deep convolutional encoder decoder architecture for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "cnn features off the shelf an astounding baseline for recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

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)