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 "kernelized deep convolutional neural network for describing complex images"):
  Abstract: With the impressive capability to capture visual content, deep convolutional neural networks (CNN) have demon- strated promising performance in various vision-based ap- plications, such as classification, recognition, and objec- t detection. However, due to the intrinsic structure design of CNN, for images with complex content, it achieves lim- ited capability on invariance to translation, rotation, and re-sizing changes, which is strongly emphasized in the s- cenario of content-based image retrieval. In this paper, to address this problem, we proposed a new kernelized deep convolutional neural network. We first discuss our motiva- tion by an experimental study to demonstrate the sensitivi- ty of the global CNN feature to the basic geometric trans- formations. Then, we propose to represent visual content with approximate invariance to the above geometric trans- formations from a kernelized perspective. We extract CNN features on the detected object-like patches and aggregate these patch-level CNN features to form a vectorial repre- sentation with the Fisher vector model. The effectiveness of our proposed algorithm is demonstrated on image search application with three benchmark datasets.

Includes members:
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "densenet implementing efficient convnet descriptor pyramids":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fdftnet facing off fake images using fake detection fine tuning network")
  Member "do convnets learn correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding and visualizing weaknesses of deep reinforcement learning agents")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "neural codes for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "multi scale orderless pooling of deep convolutional activation features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "understanding image representations by measuring their equivariance and equivalence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incorporating symmetry into deep dynamics models for improved generalization")
  Member "persistent evidence of local image properties in generic convnets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep deformation network for object landmark localization")

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)