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: "the application of two level attention models in deep convolutional neural network for fine grained image classification"
  Abstract: Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what). #R##N#In this paper, we propose to apply visual attention to fine-grained classification task using deep neural network. Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts. We combine these attentions to train domain-specific deep nets, then use it to improve both the what and where aspects. Importantly, we avoid using expensive annotations like bounding box or part information from end-to-end. The weak supervision constraint makes our work easier to generalize. #R##N#We have verified the effectiveness of the method on the subsets of ILSVRC2012 dataset and CUB200_2011 dataset. Our pipeline delivered significant improvements and achieved the best accuracy under the weakest supervision condition. The performance is competitive against other methods that rely on additional annotations.

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 "fine grained visual textual representation learning"):
  with members:
    - "one shot fine grained instance retrieval"
    - "fine grained image classification via combining vision and language"
    - "low rank bilinear pooling for fine grained classification"
    - "learning deep representations of fine grained visual descriptions"
    - "generative adversarial text to image synthesis"
    - "weakly supervised fine grained categorization with part based image representation"
    - "hierarchical question image co attention for visual question answering"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

Connected through hyperedge HE-3 (induced by source paper "fine grained image classification and retrieval by combining visual and locally pooled textual features"):
  with members:
    - "towards vqa models that can read"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "on the variance of the adaptive learning rate and beyond"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "lookahead optimizer k steps forward 1 step back"
    - "single shot scene text retrieval"
    - "an end to end textspotter with explicit alignment and attention"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "dropblock a regularization method for convolutional networks"
    - "learning to navigate for fine grained classification"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "low rank bilinear pooling for fine grained classification"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "a question centric model for visual question answering in medical imaging"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bach grand challenge on breast cancer histology images"
    - "concept centric visual turing tests for method validation"
    - "overcoming data limitation in medical visual question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "visual question answering datasets algorithms and future challenges"

Connected through hyperedge HE-6 (induced by source paper "i am going mad maximum discrepancy competition for comparing classifiers adaptively"):
  with members:
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "exploring the limits of weakly supervised pretraining"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-7 (induced by source paper "multi modal attention network learning for semantic source code retrieval"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "multimodal dual attention memory for video story question answering"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "a deep reinforced model for abstractive summarization"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-8 (induced by source paper "on the detection of digital face manipulation"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "advfaces adversarial face synthesis"
    - "deep tree learning for zero shot face anti spoofing"
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"

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)