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 "learning transferable deep models for land use classification with high resolution remote sensing images"):
  Abstract: In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-use mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-use classification with heterogeneous and high-resolution remote sensing images. In this paper, we propose a scheme to learn transferable deep models for land-use classification with HRRS images. The main idea is to rely on deep neural networks for presenting the semantic information contained in different types of land-uses and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-use dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high classification probability are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. In order to obtain a pixel-wise land-use classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-use dataset containing $150$ Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, and Google Earth images, show encouraging results and demonstrate the efficiency of the proposed scheme.

Includes members:
  Member "large scale land cover classification in gaofen 2 satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution")
  Member "texture characterization using shape co occurrence patterns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "land cover classification with high resolution remote sensing images using transferable deep models")
  Member "dota a large scale dataset for object detection in aerial images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "exploiting deep features for remote sensing image retrieval a systematic investigation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "land cover classification with high resolution remote sensing images using transferable deep models")
  Member "dense semantic labeling of subdecimeter resolution images with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "high resolution aerial image labeling with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "visual descriptors for content based retrieval of remote sensing images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "enhancing remote sensing image retrieval using a triplet deep metric learning network")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)