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: "strategies for structuring story generation"
  Abstract: Writers generally rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce new models which decompose stories by abstracting over actions and entities. The model first generates the predicate-argument structure of the text, where different mentions of the same entity are marked with placeholder tokens. It then generates a surface realization of the predicate-argument structure, and finally replaces the entity placeholders with context-sensitive names and references. Human judges prefer the stories from our models to a wide range of previous approaches to hierarchical text generation. Extensive analysis shows that our methods can help improve the diversity and coherence of events and entities in generated stories.

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 "fairseq a fast extensible toolkit for sequence modeling"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "fast parametric learning with activation memorization"
    - "weighted transformer network for machine translation"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "self attention with relative position representations"
    - "bottom up abstractive summarization"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "scaling neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "multi granularity self attention for neural machine translation"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "area attention"
    - "information aggregation for multi head attention with routing by agreement"
    - "towards better modeling hierarchical structure for self attention with ordered neurons"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "modeling recurrence for transformer"
    - "phrase based attentions"
    - "bi directional block self attention for fast and memory efficient sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "generating diverse story continuations with controllable semantics"):
  with members:
    - "writerforcing generating more interesting story endings"
    - "unsupervised text style transfer using language models as discriminators"
    - "narrative modeling with memory chains and semantic supervision"
    - "allennlp a deep semantic natural language processing platform"
    - "hierarchical neural story generation"
    - "texygen a benchmarking platform for text generation models"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-5 (induced by source paper "learning to predict explainable plots for neural story generation"):
  with members:
    - "plan write and revise an interactive system for open domain story generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "a tutorial on deep latent variable models of natural language"
    - "improving the transformer translation model with document level context"
    - "hierarchical text generation using an outline"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"

Connected through hyperedge HE-6 (induced by source paper "designing a symbolic intermediate representation for neural surface realization"):
  with members:
    - "universal dependency parsing from scratch"
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "neural text generation from rich semantic representations"
    - "generating high quality surface realizations using data augmentation and factored sequence models"
    - "human vs automatic metrics on the importance of correlation design"
    - "decoupling strategy and generation in negotiation dialogues"
    - "data to text generation with content selection and planning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"

Connected through hyperedge HE-7 (induced by source paper "induction and reference of entities in a visual story"):
  with members:
    - "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"
    - "using inter sentence diverse beam search to reduce redundancy in visual storytelling"
    - "stories for images in sequence by using visual and narrative components"

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)