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 "unsupervised part mining for fine grained image classification"):
  Abstract: Fine-grained image classification remains challenging due to the large intra-class variance and small inter-class variance. Since the subtle visual differences are only in local regions of discriminative parts among subcategories, part localization is a key issue for fine-grained image classification. Most existing approaches localize object or parts in an image with object or part annotations, which are expensive and labor-consuming. To tackle this issue, we propose a fully unsupervised part mining (UPM) approach to localize the discriminative parts without even image-level annotations, which largely improves the fine-grained classification performance. We first utilize pattern mining techniques to discover frequent patterns, i.e., co-occurrence highlighted regions, in the feature maps extracted from a pre-trained convolutional neural network (CNN) model. Inspired by the fact that these relevant meaningful patterns typically hold appearance and spatial consistency, we then cluster the mined regions to obtain the cluster centers and the discriminative parts surrounding the cluster centers are generated. Importantly, any annotations and sophisticated training procedures are not used in our proposed part localization approach. Finally, a multi-stream classification network is built for aggregating the original, object-level and part-level features simultaneously. Compared with other state-of-the-art approaches, our UPM approach achieves the competitive performance.

Includes members:
  Member "object part attention model for fine grained image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "multi attention multi class constraint for fine grained image 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 "fine grained discriminative localization via saliency guided faster r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining objects fully unsupervised object discovery and localization from a single image")
  Member "diversified visual attention networks for fine grained object classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "fully convolutional attention networks for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic region pooling make attention more expressive")
  Member "mining discriminative triplets of patches for fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization")

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)