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: "an empirical study of language cnn for image captioning"
  Abstract: Language Models based on recurrent neural networks have dominated recent image caption generation tasks. In this paper, we introduce a Language CNN model which is suitable for statistical language modeling tasks and shows competitive performance in image captioning. In contrast to previous models which predict next word based on one previous word and hidden state, our language CNN is fed with all the previous words and can model the long-range dependencies of history words, which are critical for image captioning. The effectiveness of our approach is validated on two datasets MS COCO and Flickr30K. Our extensive experimental results show that our method outperforms the vanilla recurrent neural network based language models and is competitive with the state-of-the-art methods.

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 "unpaired image captioning via scene graph alignments"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "scene graph generation with external knowledge and image reconstruction"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"
    - "engaging image captioning via personality"

Connected through hyperedge HE-3 (induced by source paper "unpaired cross lingual image caption generation with self supervised rewards"):
  with members:
    - "unsupervised bilingual lexicon induction from mono lingual multimodal data"
    - "good news everyone context driven entity aware captioning for news images"
    - "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
    - "discriminability objective for training descriptive captions"
    - "context aware visual policy network for sequence level image captioning"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"

Connected through hyperedge HE-4 (induced by source paper "scene graph generation with external knowledge and image reconstruction"):
  with members:
    - "semantic correlation promoted shape variant context for segmentation"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "graph r cnn for scene graph generation"
    - "scene graph parsing as dependency parsing"
    - "auto encoding scene graphs for image captioning"
    - "unpaired image captioning by language pivoting"
    - "image generation from scene graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-5 (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-6 (induced by source paper "shuffle then assemble learning object agnostic visual relationship features"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "context aware visual policy network for sequence level image captioning"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "vip cnn visual phrase guided convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "recurrent fusion network for image captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "reconstruction network for video captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"

Connected through hyperedge HE-8 (induced by source paper "auto encoding scene graphs for image captioning"):
  with members:
    - "panoptic segmentation"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "neural baby talk"
    - "learning to count objects in natural images for visual question answering"
    - "context aware visual policy network for sequence level image captioning"
    - "learning to compose dynamic tree structures for visual contexts"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)