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: "move forward and tell a progressive generator of video descriptions"
  Abstract: We present an efficient framework that can generate a coherent paragraph to describe a given video. Previous works on video captioning usually focus on video clips. They typically treat an entire video as a whole and generate the caption conditioned on a single embedding. On the contrary, we consider videos with rich temporal structures and aim to generate paragraph descriptions that can preserve the story flow while being coherent and concise. Towards this goal, we propose a new approach, which produces a descriptive paragraph by assembling temporally localized descriptions. Given a video, it selects a sequence of distinctive clips and generates sentences thereon in a coherent manner. Particularly, the selection of clips and the production of sentences are done jointly and progressively driven by a recurrent network -- what to describe next depends on what have been said before. Here, the recurrent network is learned via self-critical sequence training with both sentence-level and paragraph-level rewards. On the ActivityNet Captions dataset, our method demonstrated the capability of generating high-quality paragraph descriptions for videos. Compared to those by other methods, the descriptions produced by our method are often more relevant, more coherent, and more concise.

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"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"

Connected through hyperedge HE-2 (induced by source paper "streamlined dense video captioning"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "stack captioning coarse to fine learning for image captioning"

Connected through hyperedge HE-3 (induced by source paper "captioning near future activity sequences"):
  with members:
    - "when will you do what anticipating temporal occurrences of activities"
    - "dense captioning events in videos"
    - "estimating human intent for physical human robot co manipulation"
    - "towards automatic learning of procedures from web instructional videos"
    - "recurrent topic transition gan for visual paragraph generation"
    - "spice semantic propositional image caption evaluation"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "recognizing fine grained and composite activities using hand centric features and script data"

Connected through hyperedge HE-4 (induced by source paper "recursive visual sound separation using minus plus net"):
  with members:
    - "feature intertwiner for object detection"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "a neural compositional paradigm for image captioning"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "neural network encapsulation"
    - "audio visual event localization in unconstrained videos"

Connected through hyperedge HE-5 (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"
    - "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"
    - "learning comment generation by leveraging user generated data"

Connected through hyperedge HE-6 (induced by source paper "activitynet 2019 task 3 exploring contexts for dense captioning events in videos"):
  with members:
    - "streamlined dense video captioning"
    - "ruc cmu system report for dense captioning events in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dense captioning events in videos"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "cnn architectures for large scale audio classification"
    - "self critical sequence training for image captioning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "adversarial inference for multi sentence video description"):
  with members:
    - "video to video synthesis"
    - "grounding visual explanations"
    - "less is more picking informative frames for video captioning"
    - "improving image captioning with conditional generative adversarial nets"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "improving reinforcement learning based image captioning with natural language prior"
    - "learning to write with cooperative discriminators"
    - "bidirectional attentive fusion with context gating for dense video captioning"

Connected through hyperedge HE-8 (induced by source paper "grounded video description"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"

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)