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 "fine grained visual recognition with batch confusion norm"):
  Abstract: We introduce a regularization concept based on the proposed Batch Confusion Norm (BCN) to address Fine-Grained Visual Classification (FGVC). The FGVC problem is notably characterized by its two intriguing properties, significant inter-class similarity and intra-class variations, which cause learning an effective FGVC classifier a challenging task. Inspired by the use of pairwise confusion energy as a regularization mechanism, we develop the BCN technique to improve the FGVC learning by imposing class prediction confusion on each training batch, and consequently alleviate the possible overfitting due to exploring image feature of fine details. In addition, our method is implemented with an attention gated CNN model, boosted by the incorporation of Atrous Spatial Pyramid Pooling (ASPP) to extract discriminative features and proper attentions. To demonstrate the usefulness of our method, we report state-of-the-art results on several benchmark FGVC datasets, along with comprehensive ablation comparisons.

Includes members:
  Member "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification")
  Member "weakly supervised complementary parts models for fine grained image classification from the bottom up":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification")
  Member "object part attention model for fine grained image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "learning to navigate for fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "free form image inpainting with gated convolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "hierarchical bilinear pooling for fine grained visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification")
  Member "improved bilinear pooling with cnns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "pairwise confusion for fine grained visual classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the devil is in the channels mutual channel loss for fine grained image classification")

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)