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 "deep feature representations for high resolution remote sensing imagery retrieval"):
  Abstract: In this letter, we investigate how to extract deep feature representations based on convolutional neural networks (CNN) for high-resolution remote-sensing imagery retrieval (HRRSIR). Two effective schemes are proposed to generate the final feature rep-resentations for similarity measure. In the first scheme, the deep features are extracted from the fully-connected and convolutional layers of the pre-trained CNN models, respectively; in the second scheme, we fine-tune the pre-trained CNN model using the target remote sensing dataset to learn dataset-specific features. The deep feature representations generated by the two schemes are evalu-ated on two public and challenging datasets. The experimental results indicate that the proposed schemes are able to achieve state-of-the-art performance due to the good transferability of the CNN models.

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 "visual descriptors for content based retrieval of remote sensing images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "enhancing remote sensing image retrieval using a triplet deep metric learning network")
  Member "exploiting local features from deep networks for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "land use classification in remote sensing images by convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "temporal interpolation of geostationary satellite imagery with task specific optical flow")
  Member "a practical guide to cnns and fisher vectors for image instance retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a comprehensive analysis of deep regression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "return of the devil in the details delving deep into convolutional nets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep hashing methods")
  Member "matconvnet convolutional neural networks for matlab":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding")

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)