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 "bbn bilateral branch network with cumulative learning for long tailed visual recognition"):
  Abstract: Our work focuses on tackling the challenging but natural visual recognition task of long-tailed data distribution (\ie, a few classes occupy most of the data, while most classes have rarely few samples). In the literature, class re-balancing strategies (\eg, re-weighting and re-sampling) are the prominent and effective methods proposed to alleviate the extreme imbalance for dealing with long-tailed problems. In this paper, we firstly discover that these re-balancing methods achieving satisfactory recognition accuracy owe to that they could significantly promote the classifier learning of deep networks. However, at the same time, they will unexpectedly damage the representative ability of the learned deep features to some extent. Therefore, we propose a unified Bilateral-Branch Network (BBN) to take care of both representation learning and classifier learning simultaneously, where each branch does perform its own duty separately. In particular, our BBN model is further equipped with a novel cumulative learning strategy, which is designed to first learn the universal patterns and then pay attention to the tail data gradually. Extensive experiments on four benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed BBN can significantly outperform state-of-the-art methods. Furthermore, validation experiments can demonstrate both our preliminary discovery and effectiveness of tailored designs in BBN for long-tailed problems. Our method won the first place in the iNaturalist 2019 large scale species classification competition, and our code is open-source and available at this https URL

Includes members:
  Member "lvis a dataset for large vocabulary instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "equalization loss for long tailed object recognition")
  Member "large scale long tailed recognition in an open world":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incremental few shot object detection")
  Member "class balanced loss based on effective number of samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "deep learning for fine grained image analysis a survey":
    also connected through other hyperedges:
      (none)
  Member "rpc a large scale retail product checkout dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking object detection in retail stores")
  Member "what is the effect of importance weighting in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "a systematic study of the class imbalance problem in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "the devil is in the tails fine grained classification in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")

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)