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: "plan and write towards better automatic storytelling"
  Abstract: Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events. Despite considerable efforts on automatic story generation in the past, prior work either is restricted in plot planning, or can only generate stories in a narrow domain. In this paper, we explore open-domain story generation that writes stories given a title (topic) as input. We propose a plan-and-write hierarchical generation framework that first plans a storyline, and then generates a story based on the storyline. We compare two planning strategies. The dynamic schema interweaves story planning and its surface realization in text, while the static schema plans out the entire storyline before generating stories. Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations.

Connected through hyperedge HE-1 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "long and diverse text generation with planning based hierarchical variational model"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "ernie enhanced representation through knowledge integration"
    - "do massively pretrained language models make better storytellers"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (induced by source paper "what makes a good story designing composite rewards for visual storytelling"):
  with members:
    - "uniter learning universal image text representations"
    - "learning end to end goal oriented dialog with multiple answers"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "learning neural templates for text generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "hierarchical neural story generation"

Connected through hyperedge HE-3 (induced by source paper "improving neural story generation by targeted common sense grounding"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "the curious case of neural text degeneration"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "the natural language decathlon multitask learning as question answering"
    - "deep contextualized word representations"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"

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"
    - "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 "a hierarchical approach for visual storytelling using image description"):
  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"
    - "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-6 (induced by source paper "story realization expanding plot events into sentences"):
  with members:
    - "controllable neural story plot generation via reinforcement learning"
    - "hierarchical neural story generation"
    - "event representations for automated story generation with deep neural nets"
    - "regularizing and optimizing lstm language models"
    - "challenges in data to document generation"
    - "story generation from sequence of independent short descriptions"
    - "guided open vocabulary image captioning with constrained beam search"
    - "skip thought vectors"

Connected through hyperedge HE-7 (induced by source paper "pun generation with surprise"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "hierarchical neural story generation"
    - "sounding board a user centric and content driven social chatbot"
    - "generating sentences by editing prototypes"
    - "style transfer in text exploration and evaluation"
    - "pointer sentinel mixture models"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "adam a method for stochastic optimization"

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)