human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning to predict explainable plots for neural story generation"):
  Abstract: Story generation is an important natural language processing task that aims to generate coherent stories automatically. While the use of neural networks has proven effective in improving story generation, how to learn to generate an explainable high-level plot still remains a major challenge. In this work, we propose a latent variable model for neural story generation. The model treats an outline, which is a natural language sentence explainable to humans, as a latent variable to represent a high-level plot that bridges the input and output. We adopt an external summarization model to guide the latent variable model to learn how to generate outlines from training data. Experiments show that our approach achieves significant improvements over state-of-the-art methods in both automatic and human evaluations.

Includes members:
  Member "plan write and revise an interactive system for open domain story generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "alter auxiliary text rewriting tool for natural language generation")
  Member "strategies for structuring story generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "step by step separating planning from realization in neural data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "paraphrase generation with latent bag of words")
  Member "a tutorial on deep latent variable models of natural language":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "paraphrase generation with latent bag of words")
  Member "improving the transformer translation model with document level context":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "hierarchical text generation using an outline":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "extractive summary as discrete latent variables")
  Member "story ending generation with incremental encoding and commonsense knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "a skeleton based model for promoting coherence among sentences in narrative story generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")

Question: Please tell me which class the source paper 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)