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: "beyond caption to narrative video captioning with multiple sentences"
  Abstract: Recent advances in image captioning task have led to increasing interests in video captioning task. However, most works on video captioning are focused on generating single input of aggregated features, which hardly deviates from image captioning process and does not fully take advantage of dynamic contents present in videos. We attempt to generate video captions that convey richer contents by temporally segmenting the video with action localization, generating multiple captions from multiple frames, and connecting them with natural language processing techniques, in order to generate a story-like caption. We show that our proposed method can generate captions that are richer in contents and can compete with state-of-the-art method without explicitly using video-level features as input.

Connected through hyperedge HE-1 (induced by source paper "unsupervised discovery of multimodal links in multi image multi sentence documents"):
  with members:
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "exploring the limits of weakly supervised pretraining"
    - "learning transferable architectures for scalable image recognition"
    - "weakly supervised learning of visual relations"
    - "segmentation from natural language expressions"

Connected through hyperedge HE-2 (induced by source paper "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"):
  with members:
    - "video description a survey of methods datasets and evaluation metrics"
    - "less is more picking informative frames for video captioning"
    - "reconstruction network for video captioning"
    - "memory augmented attention modelling for videos"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "video captioning with transferred semantic attributes"
    - "recurrent memory addressing for describing videos"
    - "semantic compositional networks for visual captioning"

Connected through hyperedge HE-3 (induced by source paper "captioning near future activity sequences"):
  with members:
    - "move forward and tell a progressive generator of video descriptions"
    - "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"

Connected through hyperedge HE-4 (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"
    - "weakly supervised dense video captioning"

Connected through hyperedge HE-5 (induced by source paper "empirical autopsy of deep video captioning frameworks"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "video description a survey of methods datasets and evaluation metrics"
    - "dense captioning events in videos"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "re evaluating automatic metrics for image captioning"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (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-7 (induced by source paper "semantic sentence embeddings for paraphrasing and text summarization"):
  with members:
    - "textually customized video summaries"
    - "batch normalized recurrent highway networks"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "sequence to sequence video to text"
    - "video paragraph captioning using hierarchical recurrent neural networks"
    - "coherent multi sentence video description with variable level of detail"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised dense video captioning"):
  with members:
    - "frame and segment level features and candidate pool evaluation for video caption generation"
    - "captioning images with diverse objects"
    - "jointly modeling embedding and translation to bridge video and language"
    - "you only look once unified real time object detection"
    - "the long short story of movie description"
    - "show attend and tell neural image caption generation with visual attention"
    - "densecap fully convolutional localization networks for dense captioning"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"

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)