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: "video captioning with multi faceted attention"
  Abstract: Recently, video captioning has been attracting an increasing amount of interest, due to its potential for improving accessibility and information retrieval. While existing methods rely on different kinds of visual features and model structures, they do not fully exploit relevant semantic information. We present an extensible approach to jointly leverage several sorts of visual features and semantic attributes. Our novel architecture builds on LSTMs for sentence generation, with several attention layers and two multimodal layers. The attention mechanism learns to automatically select the most salient visual features or semantic attributes, and the multimodal layer yields overall representations for the input and outputs of the sentence generation component. Experimental results on the challenging MSVD and MSR-VTT datasets show that our framework outperforms the state-of-the-art approaches, while ground truth based semantic attributes are able to further elevate the output quality to a near-human level.

Connected through hyperedge HE-1 (induced by source paper "video caption dataset for describing human actions in japanese"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "stair actions a video dataset of everyday home actions"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "dense captioning events in videos"
    - "towards automatic learning of procedures from web instructional videos"
    - "video captioning with transferred semantic attributes"
    - "hollywood in homes crowdsourcing data collection for activity understanding"

Connected through hyperedge HE-2 (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-3 (induced by source paper "attention is all you need for videos self attention based video summarization using universal transformers"):
  with members:
    - "universal transformers"
    - "end to end dense video captioning with masked transformer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "the kinetics human action video dataset"
    - "towards automatic learning of procedures from web instructional videos"
    - "video captioning with transferred semantic attributes"
    - "image captioning with semantic attention"

Connected through hyperedge HE-4 (induced by source paper "be concise and precise synthesizing open domain entity descriptions from facts"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "neuralreg an end to end approach to referring expression generation"
    - "generating fine grained open vocabulary entity type descriptions"
    - "get to the point summarization with pointer generator networks"
    - "a neural knowledge language model"
    - "pointing the unknown words"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "generating text from structured data with application to the biography domain"

Connected through hyperedge HE-5 (induced by source paper "characterizing the impact of using features extracted from pre trained models on the quality of video captioning sequence to sequence models"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning social image embedding with deep multimodal attention networks"
    - "video captioning with transferred semantic attributes"
    - "frame and segment level features and candidate pool evaluation for video caption generation"
    - "soundnet learning sound representations from unlabeled video"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "sequence to sequence video to text"
    - "microsoft coco captions data collection and evaluation server"

Connected through hyperedge HE-6 (induced by source paper "hierarchical lstms with adaptive attention for visual captioning"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "image captioning with semantic attention"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-7 (induced by source paper "generating fine grained open vocabulary entity type descriptions"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "get to the point summarization with pointer generator networks"
    - "supervised typing of big graphs using semantic embeddings"
    - "challenges in data to document generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "generating text from structured data with application to the biography domain"
    - "dynamic memory networks for visual and textual question answering"

Connected through hyperedge HE-8 (induced by source paper "from deterministic to generative multi modal stochastic rnns for video captioning"):
  with members:
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "recurrent topic transition gan for visual paragraph generation"
    - "semantic compositional networks for visual captioning"
    - "image captioning with semantic attention"
    - "boosting image captioning with attributes"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

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)