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: "story generation from sequence of independent short descriptions"
  Abstract: Existing Natural Language Generation (NLG) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity. Effective solutions or, at least evaluations of modern NLG paradigms for such creative tasks have been elusive, unfortunately. This paper introduces and addresses the task of coherent story generation from independent descriptions, describing a scene or an event. Towards this, we explore along two popular text-generation paradigms -- (1) Statistical Machine Translation (SMT), posing story generation as a translation problem and (2) Deep Learning, posing story generation as a sequence to sequence learning problem. In SMT, we chose two popular methods such as phrase based SMT (PB-SMT) and syntax based SMT (SYNTAX-SMT) to `translate' the incoherent input text into stories. We then implement a deep recurrent neural network (RNN) architecture that encodes sequence of variable length input descriptions to corresponding latent representations and decodes them to produce well formed comprehensive story like summaries. The efficacy of the suggested approaches is demonstrated on a publicly available dataset with the help of popular machine translation and summarization evaluation metrics.

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"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "higher order coreference resolution with coarse to fine inference"
    - "building language models for text with named entities"

Connected through hyperedge HE-3 (induced by source paper "writerforcing generating more interesting story endings"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "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"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "incorporating structured commonsense knowledge in story completion"
    - "toward controlled generation of text"

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"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"

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"
    - "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"
    - "sort story sorting jumbled images and captions into stories"

Connected through hyperedge HE-6 (induced by source paper "automated chess commentator powered by neural chess engine"):
  with members:
    - "press space to fire automatic video game tutorial generation"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "generating levels that teach mechanics"
    - "order planning neural text generation from structured data"
    - "challenges in data to document generation"
    - "deepchess end to end deep neural network for automatic learning in chess"
    - "effective approaches to attention based neural machine translation"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-7 (induced by source paper "story realization expanding plot events into sentences"):
  with members:
    - "plan and write towards better automatic storytelling"
    - "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"
    - "guided open vocabulary image captioning with constrained beam search"
    - "skip thought vectors"

Connected through hyperedge HE-8 (induced by source paper "parameter sharing decoder pair for auto composing"):
  with members:
    - "gpt based generation for classical chinese poetry"
    - "albert a lite bert for self supervised learning of language representations"
    - "generating wikipedia by summarizing long sequences"
    - "hierarchical neural story generation"
    - "event representations for automated story generation with deep neural nets"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "on the properties of neural machine translation encoder decoder approaches"

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)