human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "exploiting deep features for remote sensing image retrieval a systematic investigation"
  Abstract: Remote sensing (RS) image retrieval based on visual content is of great significance for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues of image retrieval: visual feature, similarity metric and relevance feedback. Along with the advance of these issues, the technology of RS image retrieval has been developed comparatively mature. However, due to the complexity and multiformity of high-resolution remote sensing (HRRS) images, there is still room for improvement in the current methods on HRRS data retrieval. In this paper, we analyze the three key aspects of retrieval and provide a comprehensive review on content-based RS image retrieval methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the visual feature aspect and delve how to use powerful deep representations in this task. We conduct systematic investigation on evaluating factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw instructive conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval.

Connected through hyperedge HE-1 (induced by source paper "land cover classification with high resolution remote sensing images using transferable deep models"):
  with members:
    - "large scale land cover classification in gaofen 2 satellite imagery"
    - "texture characterization using shape co occurrence patterns"
    - "dota a large scale dataset for object detection in aerial images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-2 (induced by source paper "deep hashing learning for visual and semantic retrieval of remote sensing images"):
  with members:
    - "supervised discrete hashing with relaxation"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "learning transferable deep models for land use classification with high resolution remote sensing images"):
  with members:
    - "large scale land cover classification in gaofen 2 satellite imagery"
    - "texture characterization using shape co occurrence patterns"
    - "dota a large scale dataset for object detection in aerial images"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "functional map of the world"):
  with members:
    - "learning word like units from joint audio visual analysis"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "matterport3d learning from rgb d data in indoor environments"
    - "remote sensing image scene classification benchmark and state of the art"
    - "planet photo geolocation with convolutional neural networks"
    - "youtube 8m a large scale video classification benchmark"
    - "torontocity seeing the world with a million eyes"
    - "jointly modeling embedding and translation to bridge video and language"

Question: Please tell me which class the center node 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)