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: "using inter sentence diverse beam search to reduce redundancy in visual storytelling"
  Abstract: Visual storytelling includes two important parts: coherence between the story and images as well as the story structure. For image to text neural network models, similar images in the sequence would provide close information for story generator to obtain almost identical sentence. However, repeatedly narrating same objects or events will undermine a good story structure. In this paper, we proposed an inter-sentence diverse beam search to generate a more expressive story. Comparing to some recent models of visual storytelling task, which generate story without considering the generated sentence of the previous picture, our proposed method can avoid generating identical sentence even given a sequence of similar pictures.

Connected through hyperedge HE-1 (induced by source paper "my way of telling a story persona based grounded story generation"):
  with members:
    - "contextualize show and tell a neural visual storyteller"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "informative visual storytelling with cross modal rules"):
  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"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "image captioning with semantic attention"
    - "rare association rule mining for network intrusion detection"
    - "boosting image captioning with attributes"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-3 (induced by source paper "knowledge enriched visual storytelling"):
  with members:
    - "visual story post editing"
    - "positional encoding to control output sequence length"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "hierarchical neural story generation"

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"
    - "contextualize show and tell a neural visual storyteller"
    - "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"
    - "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:
    - "contextualize show and tell a neural visual storyteller"
    - "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"
    - "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 "on how users edit computer generated visual stories"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "hierarchical neural story generation"
    - "event representations for automated story generation with deep neural nets"

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)