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: "weakly supervised dense video captioning"
  Abstract: This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences. The proposed method is trained without explicit annotation of fine-grained sentence to video region-sequence correspondence, but is only based on weak video-level sentence annotations. It differs from existing video captioning systems in three technical aspects. First, we propose lexical fully convolutional neural networks (Lexical-FCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels. Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse region-sequences based on the Lexical-FCN outputs. A winner-takes-all scheme is adopted to weakly associate sentences to region-sequences in the training phase. Third, a sequence-to-sequence learning based language model is trained with the weakly supervised information obtained through the association process. We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin.

Connected through hyperedge HE-1 (induced by source paper "multi modal dense video captioning"):
  with members:
    - "watch listen and tell multi modal weakly supervised dense event captioning"
    - "memory attended recurrent network for video captioning"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "streamlined dense video captioning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "weakly supervised dense event captioning in videos"
    - "move forward and tell a progressive generator of video descriptions"

Connected through hyperedge HE-2 (induced by source paper "spatio temporal ranked attention networks for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "less is more picking informative frames for video captioning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the sound of pixels"
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "reconstruct and represent video contents for captioning via reinforcement learning"):
  with members:
    - "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"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-4 (induced by source paper "a case study on combining asr and visual features for generating instructional video captions"):
  with members:
    - "learning video representations using contrastive bidirectional transformer"
    - "multimodal abstractive summarization for how2 videos"
    - "videobert a joint model for video and language representation learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "learning to localize and align fine grained actions to sparse instructions"
    - "a neural compositional paradigm for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "audio visual event localization in unconstrained videos"

Connected through hyperedge HE-5 (induced by source paper "watch it twice video captioning with a refocused video encoder"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "scene graph generation with external knowledge and image reconstruction"
    - "less is more picking informative frames for video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "reconstruction network for video captioning"
    - "unpaired image captioning by language pivoting"
    - "end to end video captioning with multitask reinforcement learning"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-6 (induced by source paper "diverse video captioning through latent variable expansion with conditional gan"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "describing like humans on diversity in image captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reconstruction network for video captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "convolutional image captioning"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-7 (induced by source paper "diversified co attention towards informative live video commenting"):
  with members:
    - "automatic generation of personalized comment based on user profile"
    - "deep modular co attention networks for visual question answering"
    - "livebot generating live video comments based on visual and textual contexts"
    - "move forward and tell a progressive generator of video descriptions"
    - "multi pointer co attention networks for recommendation"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "automatic article commenting the task and dataset"
    - "joint event detection and description in continuous video streams"

Connected through hyperedge HE-8 (induced by source paper "low rank hoca efficient high order cross modal attention for video captioning"):
  with members:
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "reconstruction network for video captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video captioning via hierarchical reinforcement learning"
    - "video captioning with guidance of multimodal latent topics"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "video captioning with transferred semantic attributes"

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)