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 "bag of tricks for retail product image classification"):
  Abstract: Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail execution evaluation. In this work, we present various tricks to increase accuracy of Deep Learning models on different types of retail product image classification datasets. These tricks enable us to increase the accuracy of fine tuned convnets for retail product image classification by a large margin. As the most prominent trick, we introduce a new neural network layer called Local-Concepts-Accumulation (LCA) layer which gives consistent gains across multiple datasets. Two other tricks we find to increase accuracy on retail product identification are using an instagram-pretrained Convnet and using Maximum Entropy as an auxiliary loss for classification.

Includes members:
  Member "benchmark for generic product detection a strong baseline for dense object detection":
    also connected through other hyperedges:
      (none)
  Member "robustness properties of facebook s resnext wsl models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self training with noisy student improves imagenet classification")
  Member "domain invariant hierarchical embedding for grocery products recognition":
    also connected through other hyperedges:
      (none)
  Member "pytorch an imperative style high performance deep learning library":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "maximum entropy fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation")
  Member "exploring the limits of weakly supervised pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "superpoint self supervised interest point detection and description":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "product recognition in store shelves as a sub graph isomorphism problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain invariant hierarchical embedding for grocery products recognition")

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)