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 "semantic bilinear pooling for fine grained recognition"):
  Abstract: Fine-grained recognition, e.g., vehicle identification or bird classification, naturally has specific hierarchical labels, where fine levels are always much harder to be classified than coarse levels. However, most of the recent deep learning based methods neglect the semantic structure of fine-grained objects, and do not take advantages of the traditional fine-grained recognition techniques (e.g. coarse-to-fine classification). In this paper, we propose a novel framework, i.e., semantic bilinear pooling, for fine-grained recognition with hierarchical multi-label learning. This framework can adaptively learn the semantic information from the hierarchical labels. Specifically, a generalized softmax loss is designed for the training of the proposed framework, in order to fully exploit the semantic priors via considering the relevance between adjacent levels. A variety of experiments on several public datasets show that our proposed method has very impressive performance with low feature dimensions compared to other state-of-the-art methods.

Includes members:
  Member "monet moments embedding network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "invariant tensor feature coding")
  Member "fine grained representation learning and recognition by exploiting hierarchical semantic embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "channel interaction networks for fine grained image categorization")
  Member "improved bilinear pooling with cnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "towards faster training of global covariance pooling networks by iterative matrix square root normalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "channel interaction networks for fine grained image categorization")
  Member "low rank bilinear pooling for fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fine grained visual textual representation learning")
  Member "fully convolutional attention localization networks efficient attention localization for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "learning a discriminative filter bank within a cnn for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification")
  Member "fine grained image classification by exploring bipartite graph labels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fine grained visual textual representation 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)