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 skeleton based model for promoting coherence among sentences in narrative story generation"
  Abstract: Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1% in the human evaluation. The code is available at this https URL

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 "strategies for structuring story generation"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "generating wikipedia by summarizing long sequences"
    - "higher order coreference resolution with coarse to fine inference"
    - "building language models for text with named entities"
    - "hierarchical neural story generation"

Connected through hyperedge HE-3 (induced by source paper "generating personalized recipes from historical user preferences"):
  with members:
    - "sequence aware recommender systems"
    - "discourse aware neural rewards for coherent text generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"

Connected through hyperedge HE-4 (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"
    - "strategies for structuring 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"
    - "no metrics are perfect adversarial reward learning for visual storytelling"

Connected through hyperedge HE-5 (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"
    - "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"
    - "using inter sentence diverse beam search to reduce redundancy in visual storytelling"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "dixit interactive visual storytelling via term manipulation"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "frame semantic parsing with softmax margin segmental rnns and a syntactic scaffold"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"):
  with members:
    - "deconvolution based global decoding for neural machine translation"
    - "neural baby talk"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "improved image captioning via policy gradient optimization of spider"
    - "skeleton key image captioning by skeleton attribute decomposition"
    - "visual translation embedding network for visual relation detection"
    - "semantic compositional networks for visual captioning"

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)