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: "attention for fine grained categorization"
  Abstract: This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, specifically fine-grained categorization on the Stanford Dogs data set. In this work we use an RNN of the same structure but substitute a more powerful visual network and perform large-scale pre-training of the visual network outside of the attention RNN. Most work in attention models to date focuses on tasks with toy or more constrained visual environments, whereas we present results for fine-grained categorization better than the state-of-the-art GoogLeNet classification model. We show that our model learns to direct high resolution attention to the most discriminative regions without any spatial supervision such as bounding boxes, and it is able to discriminate fine-grained dog breeds moderately well even when given only an initial low-resolution context image and narrow, inexpensive glimpses at faces and fur patterns. This and similar attention models have the major advantage of being trained end-to-end, as opposed to other current detection and recognition pipelines with hand-engineered components where information is lost. While our model is state-of-the-art, further work is needed to fully leverage the sequential input.

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 "concept whitening for interpretable image recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "the convolutional tsetlin machine"
    - "educe explaining model decisions through unsupervised concepts extraction"
    - "saccader improving accuracy of hard attention models for vision"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "on concept based explanations in deep neural networks"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "this looks like that deep learning for interpretable image recognition"

Connected through hyperedge HE-3 (induced by source paper "saccader improving accuracy of hard attention models for vision"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "attention is not explanation"
    - "do better imagenet models transfer better"
    - "feature denoising for improving adversarial robustness"
    - "regularized evolution for image classifier architecture search"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-4 (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-5 (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-6 (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"
    - "fully convolutional attention networks for fine grained recognition"
    - "mining discriminative triplets of patches for fine grained classification"

Connected through hyperedge HE-7 (induced by source paper "embryo staging with weakly supervised region selection and dynamically decoded predictions"):
  with members:
    - "context matters refining object detection in video with recurrent neural networks"
    - "asynchronous methods for deep reinforcement learning"
    - "end to end learning of action detection from frame glimpses in videos"
    - "you only look once unified real time object detection"
    - "weakly supervised deep detection networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "agile amulet real time salient object detection with contextual attention"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "structure measure a new way to evaluate foreground maps"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"

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)