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: "using descriptive video services to create a large data source for video annotation research"
  Abstract: In this work, we introduce a dataset of video annotated with high quality natural language phrases describing the visual content in a given segment of time. Our dataset is based on the Descriptive Video Service (DVS) that is now encoded on many digital media products such as DVDs. DVS is an audio narration describing the visual elements and actions in a movie for the visually impaired. It is temporally aligned with the movie and mixed with the original movie soundtrack. We describe an automatic DVS segmentation and alignment method for movies, that enables us to scale up the collection of a DVS-derived dataset with minimal human intervention. Using this method, we have collected the largest DVS-derived dataset for video description of which we are aware. Our dataset currently includes over 84.6 hours of paired video/sentences from 92 DVDs and is growing.

Connected through hyperedge HE-1 (induced by source paper "on the evaluation of intelligent process automation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "video captioning with transferred semantic attributes"
    - "a dataset for movie description"

Connected through hyperedge HE-2 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "unsupervised learning and segmentation of complex activities from video"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-3 (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"
    - "video description a survey of methods datasets and evaluation metrics"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "m vad names a dataset for video captioning with naming"):
  with members:
    - "end to end face detection and cast grouping in movies using erd h o s r e nyi clustering"
    - "generating descriptions with grounded and co referenced people"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "moviegraphs towards understanding human centric situations from videos"
    - "dense captioning events in videos"
    - "learning from video and text via large scale discriminative clustering"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "improving lstm based video description with linguistic knowledge mined from text"

Connected through hyperedge HE-5 (induced by source paper "hierarchical lstms with adaptive attention for visual captioning"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "image captioning with semantic attention"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "video captioning with multi faceted attention"

Connected through hyperedge HE-6 (induced by source paper "livebot generating live video comments based on visual and textual contexts"):
  with members:
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "attention is all you need"
    - "weakly supervised dense video captioning"
    - "multi task video captioning with video and entailment generation"
    - "analyzing the behavior of visual question answering models"
    - "frame and segment level features and candidate pool evaluation for video caption generation"

Connected through hyperedge HE-7 (induced by source paper "mtle a multitask learning encoder of visual feature representations for video and movie description"):
  with members:
    - "consistent multitask learning with nonlinear output relations"
    - "multi task video captioning with video and entailment generation"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "vqa visual question answering"
    - "the long short story of movie description"
    - "skip thought vectors"

Connected through hyperedge HE-8 (induced by source paper "stories for images in sequence by using visual and narrative components"):
  with members:
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "vqa visual question answering"
    - "a dataset for movie description"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "are you talking to a machine dataset and methods for multilingual image question answering"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "on the properties of neural machine translation encoder decoder approaches"

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)