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 localization networks efficient attention localization for fine grained recognition"
  Abstract: Fine-grained recognition is challenging mainly because the inter-class differences between fine-grained classes are usually local and subtle while intra-class differences could be large due to pose variations. In order to distinguish them from intra-class variations, it is essential to zoom in on highly discriminative local regions. In this work, we introduce a reinforcement learning-based fully convolutional attention localization network to adaptively select multiple task-driven visual attention regions. We show that zooming in on the selected attention regions significantly improves the performance of fine-grained recognition. Compared to previous reinforcement learning-based models, the proposed approach is noticeably more computationally efficient during both training and testing because of its fully-convolutional architecture, and it is capable of simultaneous focusing its glimpse on multiple visual attention regions. The experiments demonstrate that the proposed method achieves notably higher classification accuracy on three benchmark fine-grained recognition datasets: Stanford Dogs, Stanford Cars, and CUB-200-2011.

Connected through hyperedge HE-1 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "learn to pay attention"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "daf net a saliency based weakly supervised method of dual attention fusion for fine grained image classification"):
  with members:
    - "global second order pooling convolutional networks"
    - "yolov3 an incremental improvement"
    - "learning to navigate for fine grained classification"
    - "focal loss for dense object detection"
    - "improved bilinear pooling with cnns"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "diversified visual attention networks for fine grained object classification"
    - "low rank bilinear pooling for fine grained classification"

Connected through hyperedge HE-3 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "fine grained classification of cervical cells using morphological and appearance based convolutional neural networks"
    - "maximum entropy fine grained classification"
    - "adaptive neural trees"

Connected through hyperedge HE-4 (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-5 (induced by source paper "weakly supervised complementary parts models for fine grained image classification from the bottom up"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "multi attention multi class constraint for fine grained image recognition"
    - "features for multi target multi camera tracking and re identification"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-6 (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-7 (induced by source paper "saliency for fine grained object recognition in domains with scarce training data"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep visual attention prediction"
    - "feature space transfer for data augmentation"
    - "dynamic few shot visual learning without forgetting"
    - "deep layer aggregation"
    - "prototypical networks for few shot learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "fine grained categorization via cnn based automatic extraction and integration of object level and part level features"

Connected through hyperedge HE-8 (induced by source paper "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "facial landmarks detection by self iterative regression based landmarks attention network"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "learning to navigate for fine grained classification"
    - "adversarial complementary learning for weakly supervised object localization"
    - "autoaugment learning augmentation policies from data"
    - "improved bilinear pooling with cnns"
    - "pairwise confusion for fine grained visual 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)