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 event captioning in videos"
  Abstract: Dense event captioning aims to detect and describe all events of interest contained in a video. Despite the advanced development in this area, existing methods tackle this task by making use of dense temporal annotations, which is dramatically source-consuming. This paper formulates a new problem: weakly supervised dense event captioning, which does not require temporal segment annotations for model training. Our solution is based on the one-to-one correspondence assumption, each caption describes one temporal segment, and each temporal segment has one caption, which holds in current benchmark datasets and most real-world cases. We decompose the problem into a pair of dual problems: event captioning and sentence localization and present a cycle system to train our model. Extensive experimental results are provided to demonstrate the ability of our model on both dense event captioning and sentence localization in videos.

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"
    - "move forward and tell a progressive generator of video descriptions"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"

Connected through hyperedge HE-2 (induced by source paper "look closer to ground better weakly supervised temporal grounding of sentence in video"):
  with members:
    - "weakly supervised video moment retrieval from text queries"
    - "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"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "grounding referring expressions in images by variational context"

Connected through hyperedge HE-3 (induced by source paper "learning to generate grounded image captions without localization supervision"):
  with members:
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "cycle consistency for robust visual question answering"
    - "grounding visual explanations"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "turbo learning for captionbot and drawingbot"
    - "neural baby talk"
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "grounded video description"

Connected through hyperedge HE-4 (induced by source paper "wslln weakly supervised natural language localization networks"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "temporal recurrent networks for online action detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "c wsl count guided weakly supervised localization"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-5 (induced by source paper "graph convolutional networks for temporal action localization"):
  with members:
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "end to end learning of motion representation for video understanding"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "person re identification with deep similarity guided graph neural network"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "videos as space time region graphs"
    - "rethinking the faster r cnn architecture for temporal action localization"

Connected through hyperedge HE-6 (induced by source paper "watch listen and tell multi modal weakly supervised dense event captioning"):
  with members:
    - "an attempt towards interpretable audio visual video captioning"
    - "learning to localize sound source in visual scenes"
    - "indoor sound source localization with probabilistic neural network"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised video moment retrieval via semantic completion network"):
  with members:
    - "weakly supervised video moment retrieval from text queries"
    - "cross modal interaction networks for query based moment retrieval in videos"
    - "mass masked sequence to sequence pre training for language generation"
    - "language models with transformers"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "multilevel language and vision integration for text to clip retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "to find where you talk temporal sentence localization in video with attention based location regression"):
  with members:
    - "to create what you tell generating videos from captions"
    - "single shot temporal action detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "dense captioning events in videos"
    - "tall temporal activity localization via language query"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "localizing moments in video with natural language"
    - "grounding of textual phrases in images by reconstruction"

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)