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 "efficiently utilizing complex valued polsar image data via a multi task deep learning framework"):
  Abstract: Accompanied by the successful progress of deep representation learning, convolutional neural networks (CNNs) have been widely applied to improve the accuracy of polarimetric synthetic aperture radar (PolSAR) image classification. However, in most applications, the difference between PolSAR image and optical image is rarely considered. The design of most existing network structures is not tailored to the characteristics of PolSAR image data and complex-valued data of PolSAR image are simply equated to real-valued data to adapt to the existing mainstream network pipeline to avoid complex-valued operations. These make CNNs unable to perform their full capabilities in the PolSAR image classification tasks. In this paper, we focus on finding a better input form of PolSAR image data and designing special CNN structures that are more compatible with PolSAR image. Considering the relationship between complex number and its amplitude and phase, we extract the amplitude and phase of the complex-valued PolSAR image data as input to maintain the integrity of the original information while avoiding the current immature complex-valued operations, and a novel multi-task CNN framework is proposed to adapt to novel form of input data. Furthermore, in order to better explore the unique phase information in the PolSAR image data, depthwise separable convolutions are applied to the proposed multi-task CNN model. Experiments on three benchmark datasets not only prove that using amplitude and phase information as input does contribute to the improvement of classification accuracy, but also verify the effectiveness of the proposed methods for amplitude and phase input.

Includes members:
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "polarimetric convolutional network for polsar image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automatic design of cnns via differentiable neural architecture search for polsar image classification")
  Member "learning spatio temporal representation with pseudo 3d residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "hierarchical representations for efficient architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "sar image despeckling using a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optical fringe patterns filtering based on multi stage convolution neural network")

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)