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 "crime mapping from satellite imagery via deep learning"):
  Abstract: Ensuring urban safety is an essential part of developing sustainable cities. An urban safety map can assist cities to prevent future crimes. However, mapping is costly in terms of both time and money due to the need for manual data collection. On the other hand, satellite imagery is becoming increasingly abundant and accessible with higher resolution. Given the outstanding success deep learning has achieved in the field of computer vision and pattern recognition over the past 5 years, in this paper we attempt to investigate the use of deep learning to predict crime rate directly from raw satellite imagery. To this end, we have trained a deep Convolutional Neural Network on satellite images obtained from over one million crime-incident reports collected by the Chicago Police Department. The best performing model predicted crime rate from raw satellite imagery with an accuracy of 79%. To test their reusability, we used the learned Chicago models to predict for the cities of Denver and San Francisco city-scale maps indicating crime rate in three different levels. Compared to maps made from years' worth of data collected by the corresponding police departments, our maps have an accuracy of 72% and 70%, respectively.

Includes members:
  Member "deep learning the city quantifying urban perception at a global scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "bird species categorization using pose normalized deep convolutional nets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fine grained visual textual representation learning")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "cnn features off the shelf an astounding baseline for recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "decaf a deep convolutional activation feature for generic visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crl class representative learning for image classification")
  Member "recognizing image style":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a content transformation block for image style transfer")

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.CY(Computers and Society)