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: "video description a survey of methods datasets and evaluation metrics"
  Abstract: Video description is the automatic generation of natural language sentences that describe the contents of a given video. It has applications in human-robot interaction, helping the visually impaired and video subtitling. The past few years have seen a surge of research in this area due to the unprecedented success of deep learning in computer vision and natural language processing. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, calling the need for a comprehensive survey to focus research efforts in this flourishing new direction. This article fills the gap by surveying the state-of-the-art approaches with a focus on deep learning models; comparing benchmark datasets in terms of their domains, number of classes, and repository size; and identifying the pros and cons of various evaluation metrics, such as SPICE, CIDEr, ROUGE, BLEU, METEOR, and WMD. Classical video description approaches combined subject, object, and verb detection with template-based language models to generate sentences. However, the release of large datasets revealed that these methods cannot cope with the diversity in unconstrained open domain videos. Classical approaches were followed by a very short era of statistical methods that were soon replaced with deep learning, the current state-of-the-art in video description. Our survey shows that despite the fast-paced developments, video description research is still in its infancy due to the following reasons: Analysis of video description models is challenging, because it is difficult to ascertain the contributions towards accuracy or errors of the visual features and the adopted language model in the final description. Existing datasets neither contain adequate visual diversity nor complexity of linguistic structures. Finally, current evaluation metrics fall short of measuring the agreement between machine-generated descriptions with that of humans. We conclude our survey by listing promising future research directions.

Connected through hyperedge HE-1 (induced by source paper "multimodal matching transformer for live commenting"):
  with members:
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "livebot generating live video comments based on visual and textual contexts"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "video captioning via hierarchical reinforcement learning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "see hear and read deep aligned representations"

Connected through hyperedge HE-2 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "nocaps novel object captioning at scale"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "how2 a large scale dataset for multimodal language understanding"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "reconstruction network for video captioning"
    - "memory augmented attention modelling for videos"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "video captioning with transferred semantic attributes"
    - "recurrent memory addressing for describing videos"
    - "semantic compositional networks for visual captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-4 (induced by source paper "empirical autopsy of deep video captioning frameworks"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "dense captioning events in videos"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "re evaluating automatic metrics for image captioning"
    - "enriching word vectors with subword information"
    - "beyond caption to narrative video captioning with multiple sentences"

Connected through hyperedge HE-5 (induced by source paper "meaning guided video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "yolov3 an incremental improvement"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "a comprehensive survey of deep learning for image captioning"
    - "reconstruction network for video captioning"
    - "visual question generation as dual task of visual question answering"
    - "attention is all you need"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "imperial college london submission to vatex video captioning task"):
  with members:
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "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"

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)