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 "fast fine grained image classification via weakly supervised discriminative localization"):
  Abstract: Fine-grained image classification is to recognize hundreds of subcategories in each basic-level category. Existing methods employ discriminative localization to find the key distinctions between similar subcategories. However, they generally have two limitations: 1) discriminative localization relies on region proposal methods to hypothesize the locations of discriminative regions, which are  time-consuming  and the  bottleneck  of improving classification speed and 2) the training of discriminative localization depends on object or part annotations which are heavily  labor-consuming  and the  obstacle  of marching toward practical application. It is highly challenging to address the two limitations  simultaneously , while existing methods only focus on one of them. Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: 1) multi-level attention guided localization learning is proposed to localize discriminative regions with different focuses automatically, without using object and part annotations, avoiding the labor consumption. Different level attentions focus on different characteristics of the image, which are complementary and boost classification accuracy and 2)    $n$   -pathway end-to-end discriminative localization network is proposed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by a region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation. Both are jointly employed to simultaneously improve classification speed and eliminate dependence on object and part annotations. Comparing with state-of-the-art methods on two widely used fine-grained image classification data sets, our WSDL approach achieves the best accuracy and the efficiency of classification.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "nearest neighbour radial basis function solvers for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mining on manifolds metric learning without labels")
  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 "paying more attention to attention improving the performance of convolutional neural networks via attention transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "localizing by describing attribute guided attention localization for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition")
  Member "weakly supervised fine grained categorization with part based image representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification")

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)