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: "image captioning with deep bidirectional lstms"
  Abstract: This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are proposed to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale and vertical mirror are proposed to prevent overfitting in training deep models. We visualize the evolution of bidirectional LSTM internal states over time and qualitatively analyze how our models "translate" image to sentence. Our proposed models are evaluated on caption generation and image-sentence retrieval tasks with three benchmark datasets: Flickr8K, Flickr30K and MSCOCO datasets. We demonstrate that bidirectional LSTM models achieve highly competitive performance to the state-of-the-art results on caption generation even without integrating additional mechanism (e.g. object detection, attention model etc.) and significantly outperform recent methods on retrieval task.

Connected through hyperedge HE-1 (induced by source paper "on architectures for including visual information in neural language models for image description"):
  with members:
    - "transfer learning from language models to image caption generators better models may not transfer better"
    - "visual entailment a novel task for fine grained image understanding"
    - "do better imagenet models transfer better"
    - "quantifying the amount of visual information used by neural caption generators"
    - "pre gen metrics predicting caption quality metrics without generating captions"
    - "object counts bringing explicit detections back into image captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "grounded textual entailment"

Connected through hyperedge HE-2 (induced by source paper "comic toward a compact image captioning model with attention"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "contrastive learning for image captioning"
    - "massive exploration of neural machine translation architectures"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "a deep decoder structure based on wordembedding regression for an encoder decoder based model for image captioning"):
  with members:
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "stack captioning coarse to fine learning for image captioning"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "skeleton key image captioning by skeleton attribute decomposition"
    - "paying more attention to saliency image captioning with saliency and context attention"
    - "semantic compositional networks for visual captioning"

Connected through hyperedge HE-4 (induced by source paper "exploring overall contextual information for image captioning in human like cognitive style"):
  with members:
    - "knowledge embedded representation learning for fine grained image recognition"
    - "neural baby talk"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "rethinking the form of latent states in image captioning"
    - "learning to parse and translate improves neural machine translation"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "convolutional image captioning"
    - "skeleton key image captioning by skeleton attribute decomposition"

Connected through hyperedge HE-5 (induced by source paper "tigs an inference algorithm for text infilling with gradient search"):
  with members:
    - "text infilling"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "detecting egregious responses in neural sequence to sequence models"
    - "a multi modal chinese poetry generation model"
    - "bidirectional beam search forward backward inference in neural sequence models for fill in the blank image captioning"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "tpsgtr neural symbolic tensor product scene graph triplet representation for image captioning"):
  with members:
    - "neural baby talk"
    - "attentive tensor product learning"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "tensor product generation networks for deep nlp modeling"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "semantic compositional networks for visual captioning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-7 (induced by source paper "predicting visual features from text for image and video caption retrieval"):
  with members:
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "generative adversarial text to image synthesis"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "learning joint representations of videos and sentences with web image search"
    - "rnn fisher vectors for action recognition and image annotation"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "eventnet a large scale structured concept library for complex event detection in video"

Connected through hyperedge HE-8 (induced by source paper "dpred making typical activation and weight values matter in deep learning computing"):
  with members:
    - "value aware quantization for training and inference of neural networks"
    - "dynamic stripes exploiting the dynamic precision requirements of activation values in neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning deep cnn denoiser prior for image restoration"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)