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: "generating video descriptions with topic guidance"
  Abstract: Generating video descriptions in natural language (a.k.a. video captioning) is a more challenging task than image captioning as the videos are intrinsically more complicated than images in two aspects. First, videos cover a broader range of topics, such as news, music, sports and so on. Second, multiple topics could coexist in the same video. In this paper, we propose a novel caption model, topic-guided model (TGM), to generate topic-oriented descriptions for videos in the wild via exploiting topic information. In addition to predefined topics, i.e., category tags crawled from the web, we also mine topics in a data-driven way based on training captions by an unsupervised topic mining model. We show that data-driven topics reflect a better topic schema than the predefined topics. As for testing video topic prediction, we treat the topic mining model as teacher to train the student, the topic prediction model, by utilizing the full multi-modalities in the video especially the speech modality. We propose a series of caption models to exploit topic guidance, including implicitly using the topics as input features to generate words related to the topic and explicitly modifying the weights in the decoder with topics to function as an ensemble of topic-aware language decoders. Our comprehensive experimental results on the current largest video caption dataset MSR-VTT prove the effectiveness of our topic-guided model, which significantly surpasses the winning performance in the 2016 MSR video to language challenge.

Connected through hyperedge HE-1 (induced by source paper "bidirectional attentive fusion with context gating for dense video captioning"):
  with members:
    - "learning to guide decoding for image captioning"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "tall temporal activity localization via language query"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "localizing moments in video with natural language"
    - "temporal dynamic graph lstm for action driven video object detection"
    - "video captioning with transferred semantic attributes"
    - "temporal action localization in untrimmed videos via multi stage cnns"

Connected through hyperedge HE-2 (induced by source paper "ruc cmu system report for dense captioning events in videos"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dense captioning events in videos"
    - "self critical sequence training for image captioning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "video captioning with guidance of multimodal latent topics"):
  with members:
    - "weakly supervised dense video captioning"
    - "improving interpretability of deep neural networks with semantic information"
    - "semantic compositional networks for visual captioning"
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "frame and segment level features and candidate pool evaluation for video caption generation"
    - "tgif a new dataset and benchmark on animated gif description"

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)