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 "linguistic versus latent relations for modeling coherent flow in paragraphs"):
  Abstract: Generating a long, coherent text such as a paragraph requires a high-level control of different levels of relations between sentences (e.g., tense, coreference). We call such a logical connection between sentences as a (paragraph) flow. In order to produce a coherent flow of text, we explore two forms of intersentential relations in a paragraph: one is a human-created linguistical relation that forms a structure (e.g., discourse tree) and the other is a relation from latent representation learned from the sentences themselves. Our two proposed models incorporate each form of relations into document-level language models: the former is a supervised model that jointly learns a language model as well as discourse relation prediction, and the latter is an unsupervised model that is hierarchically conditioned by a recurrent neural network (RNN) over the latent information. Our proposed models with both forms of relations outperform the baselines in partially conditioned paragraph generation task. Our codes and data are publicly available.

Includes members:
  Member "describing a knowledge base":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational template machine for data to text generation")
  Member "a dataset of peer reviews peerread collection insights and nlp applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "textual analysis of artificial intelligence manuscripts reveals features associated with peer review outcome")
  Member "adventure adversarial training for textual entailment with knowledge guided examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a cross sentence latent variable model for semi supervised text sequence matching")
  Member "detecting and explaining causes from text for a time series event":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation")
  Member "survey of the state of the art in natural language generation core tasks applications and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "variational neural discourse relation recognizer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural simile recognition with cyclic multitask learning and local attention")
  Member "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non autoregressive neural dialogue generation")
  Member "hierarchical multiscale recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")

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)