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 "a benchmark on tricks for large scale image retrieval"):
  Abstract: Many studies have been performed on metric learning, which has become a key ingredient in top-performing methods of instance-level image retrieval. Meanwhile, less attention has been paid to pre-processing and post-processing tricks that can significantly boost performance. Furthermore, we found that most previous studies used small scale datasets to simplify processing. Because the behavior of a feature representation in a deep learning model depends on both domain and data, it is important to understand how model behave in large-scale environments when a proper combination of retrieval tricks is used. In this paper, we extensively analyze the effect of well-known pre-processing, post-processing tricks, and their combination for large-scale image retrieval. We found that proper use of these tricks can significantly improve model performance without necessitating complex architecture or introducing loss, as confirmed by achieving a competitive result on the Google Landmark Retrieval Challenge 2019.

Includes members:
  Member "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transferring dense pose to proximal animal classes")
  Member "semi supervised feature level attribute manipulation for fashion image retrieval":
    also connected through other hyperedges:
      (none)
  Member "combination of multiple global descriptors for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semi supervised feature level attribute manipulation for fashion image retrieval")
  Member "multigrain a unified image embedding for classes and instances":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "class feature pyramids for video explanation")
  Member "efficient nearest neighbors search for large scale landmark recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "texture synthesis guided deep hashing for texture image retrieval")
  Member "bag of tricks for image classification with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "batch dropblock network for person re identification and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compounding the performance improvements of assembled techniques in a convolutional neural network")
  Member "efficient image retrieval via decoupling diffusion into online and offline processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "large scale landmark retrieval recognition under a noisy and diverse dataset")

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)