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 categorization via cnn based automatic extraction and integration of object level and part level features"):
  Abstract: Fine-grained categorization can benefit from part-based features which reveal subtle visual differences between object categories. Handcrafted features have been widely used for part detection and classification. Although a recent trend seeks to learn such features automatically using powerful deep learning models such as convolutional neural networks (CNN), their training and possibly also testing require manually provided annotations which are costly to obtain. To relax these requirements, we assume in this study a general problem setting in which the raw images are only provided with object-level class labels for model training with no other side information needed. Specifically, by extracting and interpreting the hierarchical hidden layer features learned by a CNN, we propose an elaborate CNN-based system for fine-grained categorization. When evaluated on the Caltech-UCSD Birds-200-2011, FGVC-Aircraft, Cars and Stanford dogs datasets under the setting that only object-level class labels are used for training and no other annotations are available for both training and testing, our method achieves impressive performance that is superior or comparable to the state of the art. Moreover, it sheds some light on ingenious use of the hierarchical features learned by CNN which has wide applicability well beyond the current fine-grained categorization task.

Includes members:
  Member "locally supervised deep hybrid model for scene recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scene based factored attention for image captioning")
  Member "weakly supervised fine grained categorization with part based image representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "mining discriminative triplets of patches for fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "π nets deep polynomial neural networks")
  Member "bilinear cnn models for fine grained visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "neural activation constellations unsupervised part model discovery with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interpretable cnns for object classification")
  Member "part stacked cnn for fine grained visual categorization":
    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)