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: "hierarchical text generation using an outline"
  Abstract: Many challenges in natural language processing require generating text, including language translation, dialogue generation, and speech recognition. For all of these problems, text generation becomes more difficult as the text becomes longer. Current language models often struggle to keep track of coherence for long pieces of text. Here, we attempt to have the model construct and use an outline of the text it generates to keep it focused. We find that the usage of an outline improves perplexity. We do not find that using the outline improves human evaluation over a simpler baseline, revealing a discrepancy in perplexity and human perception. Similarly, hierarchical generation is not found to improve human evaluation scores.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "extractive summary as discrete latent variables"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "achieving human parity on automatic chinese to english news translation"
    - "on accurate evaluation of gans for language generation"
    - "generating wikipedia by summarizing long sequences"
    - "discrete autoencoders for sequence models"
    - "deep contextualized word representations"
    - "hierarchical neural story generation"

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)