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: "title generation for user generated videos"
  Abstract: A great video title describes the most salient event compactly and captures the viewer's attention. In contrast, video captioning tends to generate sentences that describe the video as a whole. Although generating a video title automatically is a very useful task, it is much less addressed than video captioning. We address video title generation for the first time by proposing two methods that extend state-of-the-art video captioners to this new task. First, we make video captioners highlight sensitive by priming them with a highlight detector. Our framework allows for jointly training a model for title generation and video highlight localization. Second, we induce high sentence diversity in video captioners, so that the generated titles are also diverse and catchy. This means that a large number of sentences might be required to learn the sentence structure of titles. Hence, we propose a novel sentence augmentation method to train a captioner with additional sentence-only examples that come without corresponding videos. We collected a large-scale Video Titles in the Wild (VTW) dataset of 18100 automatically crawled user-generated videos and titles. On VTW, our methods consistently improve title prediction accuracy, and achieve the best performance in both automatic and human evaluation. Finally, our sentence augmentation method also outperforms the baselines on the M-VAD dataset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "action modifiers learning from adverbs in instructional videos"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "weakly supervised video moment retrieval from text queries"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "human action adverb recognition adha dataset and a three stream hybrid model"
    - "adversarial soft detection based aggregation network for image retrieval"
    - "motion appearance co memory networks for video question answering"
    - "attributes as operators factorizing unseen attribute object compositions"

Connected through hyperedge HE-3 (induced by source paper "multimodal abstractive summarization for how2 videos"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "how2 a large scale dataset for multimodal language understanding"
    - "product title refinement via multi modal generative adversarial learning"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "get to the point summarization with pointer generator networks"
    - "attention strategies for multi source sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "video skimming taxonomy and comprehensive survey"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "a general framework for edited video and raw video summarization"
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "summarizing first person videos from third persons points of views"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "collaborative summarization of topic related videos"

Connected through hyperedge HE-5 (induced by source paper "video summarization through human detection on a social robot"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "collaborative summarization of topic related videos"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "video summarization with long short term memory"
    - "video2gif automatic generation of animated gifs from video"

Connected through hyperedge HE-6 (induced by source paper "hierarchical recurrent neural network for video summarization"):
  with members:
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "beyond short snippets deep networks for video classification"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "sequence to sequence video to text"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "less is more picking informative frames for video captioning"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "reconstruction network for video captioning"
    - "supervising neural attention models for video captioning by human gaze data"
    - "weakly supervised dense video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "to click or not to click automatic selection of beautiful thumbnails from videos"
    - "image captioning with semantic attention"
    - "hierarchical co attention for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "comixify transform video into a comics"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "stereoscopic neural style transfer"
    - "improved style transfer by respecting inter layer correlations"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "learning transferable architectures for scalable image recognition"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deep photo style transfer"
    - "automatic semantic style transfer using deep convolutional neural networks and soft masks"

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)