human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "fine grained discriminative localization via saliency guided faster r cnn"
  Abstract: Discriminative localization is essential for fine-grained image classification task, which devotes to recognizing hundreds of subcategories in the same basic-level category. Reflecting on discriminative regions of objects, key differences among different subcategories are subtle and local. Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers. However, these methods generally have two limitations: (1) Separation of the two-stage learning is time-consuming. (2) Dependence on object and parts annotations for discriminative localization learning leads to heavily labor-consuming labeling. It is highly challenging to address these two important limitations simultaneously. Existing methods only focus on one of them. Therefore, this paper proposes the discriminative localization approach via saliency-guided Faster R-CNN to address the above two limitations at the same time, and our main novelties and advantages are: (1) End-to-end network based on Faster R-CNN is designed to simultaneously localize discriminative regions and encode discriminative features, which accelerates classification speed. (2) Saliency-guided localization learning is proposed to localize the discriminative region automatically, avoiding labor-consuming labeling. Both are jointly employed to simultaneously accelerate classification speed and eliminate dependence on object and parts annotations. Comparing with the state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach achieves both the best classification accuracy and efficiency.

Connected through hyperedge HE-1 (induced by source paper "mining objects fully unsupervised object discovery and localization from a single image"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection a survey"
    - "object part attention model for fine grained image classification"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "diversified visual attention networks for fine grained object classification"
    - "deep descriptor transforming for image co localization"

Connected through hyperedge HE-2 (induced by source paper "fast fine grained image classification via weakly supervised discriminative localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "nearest neighbour radial basis function solvers for deep neural networks"
    - "diversified visual attention networks for fine grained object classification"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "interactive inter layer activeness propagation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised part mining for fine grained image classification"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multi attention multi class constraint for fine grained image recognition"
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "fully convolutional attention networks for fine grained recognition"
    - "mining discriminative triplets of patches for fine grained classification"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-4 (induced by source paper "instance level microtubule tracking"):
  with members:
    - "object part attention model for fine grained image classification"
    - "maskrnn instance level video object segmentation"
    - "path aggregation network for instance segmentation"
    - "attention is all you need"
    - "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
    - "learning to segment every thing"
    - "mask r cnn"
    - "boundary aware instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "adversarial learning for fine grained image search"):
  with members:
    - "object part attention model for fine grained image classification"
    - "deep semantic hashing with generative adversarial networks"
    - "binary generative adversarial networks for image retrieval"
    - "one shot fine grained instance retrieval"
    - "convolutional neural network architecture for geometric matching"
    - "visual search at ebay"
    - "unsupervised image to image translation networks"
    - "lr gan layered recursive generative adversarial networks for image generation"

Connected through hyperedge HE-6 (induced by source paper "fpan fine grained and progressive attention localization network for data retrieval"):
  with members:
    - "dssd deconvolutional single shot detector"
    - "dual deep network for visual tracking"
    - "ensemble of part detectors for simultaneous classification and localization"
    - "stnet selective tuning of convolutional networks for object localization"
    - "progressive attention networks for visual attribute prediction"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "xception deep learning with depthwise separable convolutions"
    - "modeling and propagating cnns in a tree structure for visual tracking"

Connected through hyperedge HE-7 (induced by source paper "from volcano to toyshop adaptive discriminative region discovery for scene recognition"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "scene graph generation by iterative message passing"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "object level context modeling for scene classification with context cnn"
    - "locally supervised deep hybrid model for scene recognition"
    - "scene recognition with cnns objects scales and dataset bias"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"

Question: Please tell me which class the center node 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)