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: "a pipeline for creative visual storytelling"
  Abstract: Computational visual storytelling produces a textual description of events and interpretations depicted in a sequence of images. These texts are made possible by advances and cross-disciplinary approaches in natural language processing, generation, and computer vision. We define a computational creative visual storytelling as one with the ability to alter the telling of a story along three aspects: to speak about different environments, to produce variations based on narrative goals, and to adapt the narrative to the audience. These aspects of creative storytelling and their effect on the narrative have yet to be explored in visual storytelling. This paper presents a pipeline of task-modules, Object Identification, Single-Image Inferencing, and Multi-Image Narration, that serve as a preliminary design for building a creative visual storyteller. We have piloted this design for a sequence of images in an annotation task. We present and analyze the collected corpus and describe plans towards automation.

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"
    - "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-2 (induced by source paper "visual understanding and narration a deeper understanding and explanation of visual scenes"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "temporal event knowledge acquisition via identifying narratives"
    - "scoutbot a dialogue system for collaborative navigation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "dock detecting objects by transferring common sense knowledge"
    - "hierarchical neural story generation"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "the something something video database for learning and evaluating visual common sense"

Connected through hyperedge HE-3 (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 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-4 (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"
    - "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-5 (induced by source paper "a hierarchical approach for visual storytelling using image description"):
  with members:
    - "contextualize show and tell a neural visual storyteller"
    - "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"

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)