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 "context aware query image representation for particular object retrieval"):
  Abstract: The current models of image representation based on Convolutional Neural Networks (CNN) have shown tremendous performance in image retrieval. Such models are inspired by the information flow along the visual pathway in the human visual cortex. We propose that in the field of particular object retrieval, the process of extracting CNN representations from query images with a given region of interest (ROI) can also be modelled by taking inspiration from human vision. Particularly, we show that by making the CNN pay attention on the ROI while extracting query image representation leads to significant improvement over the baseline methods on challenging Oxford5k and Paris6k datasets. Furthermore, we propose an extension to a recently introduced encoding method for CNN representations, regional maximum activations of convolutions (R-MAC). The proposed extension weights the regional representations using a novel saliency measure prior to aggregation. This leads to further improvement in retrieval accuracy.

Includes members:
  Member "selective convolutional descriptor aggregation for fine grained image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "daf net a saliency based weakly supervised method of dual attention fusion for fine grained image classification")
  Member "end to end learning of deep visual representations for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "deep image retrieval learning global representations for image search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "paying more attention to attention improving the performance of convolutional neural networks via attention transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "efficient diffusion on region manifolds recovering small objects with compact cnn representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "cnn image retrieval learns from bow unsupervised fine tuning with hard examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "cross dimensional weighting for aggregated deep convolutional features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "netvlad cnn architecture for weakly supervised place recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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)