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: "contextualize show and tell a neural visual storyteller"
  Abstract: We present a neural model for generating short stories from image sequences, which extends the image description model by Vinyals et al. (Vinyals et al., 2015). This extension relies on an encoder LSTM to compute a context vector of each story from the image sequence. This context vector is used as the first state of multiple independent decoder LSTMs, each of which generates the portion of the story corresponding to each image in the sequence by taking the image embedding as the first input. Our model showed competitive results with the METEOR metric and human ratings in the internal track of the Visual Storytelling Challenge 2018.

Connected through hyperedge HE-1 (induced by source paper "visual storytelling via predicting anchor word embeddings in the stories"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "deep residual learning for image recognition"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "my way of telling a story persona based grounded story generation"):
  with members:
    - "a pipeline for creative visual storytelling"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "hierarchical neural story generation"

Connected through hyperedge HE-3 (induced by source paper "incorporating textual evidence in visual storytelling"):
  with members:
    - "using deep object features for image descriptions"
    - "learning to guide decoding for image captioning"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "object counts bringing explicit detections back into image captioning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "stories for images in sequence by using visual and narrative components"

Connected through hyperedge HE-4 (induced by source paper "induction and reference of entities in a visual story"):
  with members:
    - "strategies for structuring story generation"
    - "a pipeline for creative visual storytelling"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "plan and write towards better automatic storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "hierarchical neural story generation"
    - "using inter sentence diverse beam search to reduce redundancy in visual storytelling"
    - "stories for images in sequence by using visual and narrative components"

Connected through hyperedge HE-5 (induced by source paper "the steep road to happily ever after an analysis of current visual storytelling models"):
  with members:
    - "a pipeline for creative visual storytelling"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "using inter sentence diverse beam search to reduce redundancy in visual storytelling"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "story generation from sequence of independent short descriptions"
    - "sort story sorting jumbled images and captions into stories"

Connected through hyperedge HE-6 (induced by source paper "a hierarchical approach for visual storytelling using image description"):
  with members:
    - "a pipeline for creative visual storytelling"
    - "plan and write towards better automatic storytelling"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "stories for images in sequence by using visual and narrative components"
    - "event representations for automated story generation with deep neural nets"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "a hierarchical approach for generating descriptive image paragraphs"

Connected through hyperedge HE-7 (induced by source paper "character centric storytelling"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "semantic compositional networks for visual captioning"
    - "deep residual learning for image recognition"

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.CL(Computation and Language)