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 "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"):
  Abstract: Abstract   Benchmark datasets are critical for developing, evaluating, and comparing remote sensing image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in that (1) they were originally collected for land use/land cover classification instead of RSIR; (2) they are relatively small in terms of the number of classes as well as the number of images per class which makes them unsuitable for developing deep learning based approaches; and (3) they are not appropriate for RSIR due to the large amount of background present in the images. These limitations restrict the development of novel approaches for RSIR, particularly those based on deep learning which require large amounts of training data. We therefore present a new large-scale remote sensing dataset termed “PatternNet” that was collected specifically for RSIR. PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class. Significantly, PatternNet’s large scale makes it suitable for developing novel, deep learning based approaches for RSIR. We use PatternNet to evaluate the performance of over 35 RSIR methods ranging from traditional handcrafted feature based methods to recent, deep learning based ones. These results serve as a baseline for future research on RSIR.

Includes members:
  Member "aid a benchmark data set for performance evaluation of aerial scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis")
  Member "dense semantic labeling of subdecimeter resolution images with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep hashing learning for visual and semantic retrieval of remote sensing images")
  Member "remote sensing image scene classification benchmark and state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "selective convolutional descriptor aggregation for fine grained image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "daf net a saliency based weakly supervised method of dual attention fusion for fine grained image classification")
  Member "deep image retrieval learning global representations for image search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "fully convolutional networks for dense semantic labelling of high resolution aerial imagery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "visual descriptors for content based retrieval of remote sensing images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "enhancing remote sensing image retrieval using a triplet deep metric learning network")

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)