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 "eye in the sky real time drone surveillance system dss for violent individuals identification using scatternet hybrid deep learning network"):
  Abstract: Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.

Includes members:
  Member "generative scatternet hybrid deep learning g shdl network with structural priors for semantic image segmentation":
    also connected through other hyperedges:
      (none)
  Member "texture and structure incorporated scatternet hybrid deep learning network ts shdl for brain matter segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative scatternet hybrid deep learning g shdl network with structural priors for semantic image segmentation")
  Member "efficient convolutional network learning using parametric log based dual tree wavelet scatternet":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "harmonic networks with limited training samples")
  Member "dual tree wavelet scattering network with parametric log transformation for object classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a learnable scatternet locally invariant convolutional layers")
  Member "scatternet hybrid deep learning shdl network for object classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi path learnable wavelet neural network for image classification")
  Member "feature pyramid networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "multi resolution dual tree wavelet scattering network for signal classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a learnable scatternet locally invariant convolutional layers")
  Member "flowing convnets for human pose estimation in videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")

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)