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 "object part attention model for fine grained image classification"):
  Abstract: Fine-grained image classification is to recognize hundreds of subcategories belonging to the same basic-level category, such as 200 subcategories belonging to the bird, which is highly challenging due to  large  variance in the same subcategory and  small  variance among different subcategories. Existing methods generally first locate the objects or parts and then discriminate which subcategory the image belongs to. However, they mainly have  two limitations : 1) relying on object or part annotations which are heavily labor consuming; and 2) ignoring the spatial relationships between the object and its parts as well as among these parts, both of which are significantly helpful for finding discriminative parts. Therefore, this paper proposes the  object-part attention model (OPAM)  for weakly supervised fine-grained image classification and the main novelties are: 1)  object-part attention model  integrates two level attentions:  object-level attention  localizes objects of images, and  part-level attention  selects discriminative parts of object. Both are jointly employed to learn multi-view and multi-scale features to enhance their mutual promotion; and 2)  Object-part spatial constraint model  combines two spatial constraints:  object spatial constraint  ensures selected parts highly representative and  part spatial constraint  eliminates redundancy and enhances discrimination of selected parts. Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories. Importantly, neither object nor part annotations are used in our proposed approach, which avoids the heavy labor consumption of labeling. Compared with more than ten state-of-the-art methods on four widely-used datasets, our OPAM approach achieves the best performance.

Includes members:
  Member "diversified visual attention networks for fine grained object classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way")
  Member "fully convolutional attention localization networks efficient attention localization for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "weakly supervised fine grained categorization with part based image representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "learning a discriminative filter bank within a cnn for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification")
  Member "interactive inter layer activeness propagation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast fine grained image classification via weakly supervised discriminative localization")
  Member "mining discriminative triplets of patches for fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization")
  Member "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a metric learning reality check")
  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)