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: "vatex a large scale high quality multilingual dataset for video and language research"
  Abstract: We present a new large-scale multilingual video description dataset, VATEX, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. Compared to the widely-used MSR-VTT dataset, VATEX is multilingual, larger, linguistically complex, and more diverse in terms of both video and natural language descriptions. We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context. Extensive experiments on the VATEX dataset show that, first, the unified multilingual model can not only produce both English and Chinese descriptions for a video more efficiently, but also offer improved performance over the monolingual models. Furthermore, we demonstrate that the spatiotemporal video context can be effectively utilized to align source and target languages and thus assist machine translation. In the end, we discuss the potentials of using VATEX for other video-and-language research.

Connected through hyperedge HE-1 (induced by source paper "normalized and geometry aware self attention network for image captioning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "image captioning transforming objects into words"
    - "attention on attention for image captioning"
    - "multimodal transformer with multi view visual representation for image captioning"
    - "large scale long tailed recognition in an open world"
    - "aligning linguistic words and visual semantic units for image captioning"
    - "context aware self attention networks"
    - "deep modular co attention networks for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "tvr a large scale dataset for video subtitle moment retrieval"):
  with members:
    - "tvqa spatio temporal grounding for video question answering"
    - "temporal localization of moments in video collections with natural language"
    - "roberta a robustly optimized bert pretraining approach"
    - "excl extractive clip localization using natural language descriptions"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mattnet modular attention network for referring expression comprehension"
    - "multilevel language and vision integration for text to clip retrieval"

Connected through hyperedge HE-3 (induced by source paper "commongen a constrained text generation dataset towards generative commonsense reasoning"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "codah an adversarially authored question answer dataset for common sense"
    - "bertscore evaluating text generation with bert"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "mirrorgan learning text to image generation by redescription"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "levenshtein transformer"
    - "text infilling"

Connected through hyperedge HE-4 (induced by source paper "r fakeddit a new multimodal benchmark dataset for fine grained fake news detection"):
  with members:
    - "nela gt 2018 a large multi labelled news dataset for the study of misinformation in news articles"
    - "defending against neural fake news"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-5 (induced by source paper "cross lingual vision language navigation"):
  with members:
    - "stay on the path instruction fidelity in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "speaker follower models for vision and language navigation"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "imperial college london submission to vatex video captioning task"):
  with members:
    - "video description a survey of methods datasets and evaluation metrics"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "image captioning with semantic attention"
    - "sequence to sequence video to text"
    - "cider consensus based image description evaluation"
    - "neural machine translation by jointly learning to align and translate"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-7 (induced by source paper "integrating temporal and spatial attentions for vatex video captioning challenge 2019"):
  with members:
    - "activitynet 2019 task 3 exploring contexts for dense captioning events in videos"
    - "video classification with channel separated convolutional networks"
    - "streamlined dense video captioning"
    - "vse improving visual semantic embeddings with hard negatives"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-8 (induced by source paper "multi view features and hybrid reward strategies for vatex video captioning challenge 2019"):
  with members:
    - "video classification with channel separated convolutional networks"
    - "large scale weakly supervised pre training for video action recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "self critical sequence training for image captioning"

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)