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: "fully convolutional attention networks for fine grained recognition"
  Abstract: Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses. A key to address this problem is to localize discriminative parts to extract pose-invariant features. However, ground-truth part annotations can be expensive to acquire. Moreover, it is hard to define parts for many fine-grained classes. This work introduces Fully Convolutional Attention Networks (FCANs), a reinforcement learning framework to optimally glimpse local discriminative regions adaptive to different fine-grained domains. Compared to previous methods, our approach enjoys three advantages: 1) the weakly-supervised reinforcement learning procedure requires no expensive part annotations; 2) the fully-convolutional architecture speeds up both training and testing; 3) the greedy reward strategy accelerates the convergence of the learning. We demonstrate the effectiveness of our method with extensive experiments on four challenging fine-grained benchmark datasets, including CUB-200-2011, Stanford Dogs, Stanford Cars and Food-101.

Connected through hyperedge HE-1 (induced by source paper "stochastic region pooling make attention more expressive"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi attention multi class constraint for fine grained image recognition"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "convolutional neural networks with alternately updated clique"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "learning what and where to attend"
    - "gather excite exploiting feature context in convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "recognizing part attributes with insufficient data"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "a large scale attribute dataset for zero shot learning"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "pseudo mask augmented object detection"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "attribute recognition by joint recurrent learning of context and correlation"

Connected through hyperedge HE-3 (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"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"

Connected through hyperedge HE-4 (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"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "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"
    - "mining discriminative triplets of patches for fine grained classification"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-5 (induced by source paper "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bilinear attention networks"
    - "learning to navigate for fine grained classification"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "non local neural networks"
    - "attention is all you need"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"

Connected through hyperedge HE-6 (induced by source paper "learning where to see a novel attention model for automated immunohistochemical scoring"):
  with members:
    - "fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features"
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "action recognition using visual attention"
    - "active object localization with deep reinforcement learning"
    - "going deeper with convolutions"
    - "recurrent models of visual attention"

Connected through hyperedge HE-7 (induced by source paper "this looks like that deep learning for interpretable image recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable and steerable sequence learning via prototypes"
    - "generalizing the convolution operator in convolutional neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-8 (induced by source paper "part aware fine grained object categorization using weakly supervised part detection network"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multiple instance detection network with online instance classifier refinement"
    - "diversified visual attention networks for fine grained object classification"
    - "dynamic computational time for visual attention"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "real time fine grained categorization with accuracy and interpretability"
    - "weakly supervised fine grained categorization with part based image representation"
    - "fine grained pose prediction normalization and recognition"

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)