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 representation learning and recognition by exploiting hierarchical semantic embedding"):
  Abstract: Object categories inherently form a hierarchy with different levels of concept abstraction, especially for fine-grained categories. For example, birds (Aves) can be categorized according to a four-level hierarchy of order, family, genus, and species. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make prediction less ambiguous. However, previous studies of fine-grained image recognition primarily focus on categories of one certain level and usually overlook this correlation information. In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework. Specifically, the HSE framework sequentially predicts the category score vector of each level in the hierarchy, from highest to lowest. At each level, it incorporates the predicted score vector of the higher level as prior knowledge to learn finer-grained feature representation. During training, the predicted score vector of the higher level is also employed to regularize label prediction by using it as soft targets of corresponding sub-categories. To evaluate the proposed framework, we organize the 200 bird species of the Caltech-UCSD birds dataset with the four-level category hierarchy and construct a large-scale butterfly dataset that also covers four level categories. Extensive experiments on these two and the newly-released VegFru datasets demonstrate the superiority of our HSE framework over the baseline methods and existing competitors.

Includes members:
  Member "crowd counting using deep recurrent spatial aware network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting")
  Member "knowledge embedded representation learning for fine grained image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization")
  Member "multi label image recognition by recurrently discovering attentional regions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pedestrian attribute recognition a survey")
  Member "recurrent attentional reinforcement learning for multi label image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tree structured policy based progressive reinforcement learning for temporally language grounding in video")
  Member "diversified visual attention networks for fine grained object classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way")
  Member "learning a wavelet like auto encoder to accelerate deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a network pruning network approach to deep model compression")
  Member "fine grained image classification via combining vision and language":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fine grained visual textual representation learning")
  Member "low rank bilinear pooling for fine grained classification":
    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)