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 "style transfer for texts to err is human but error margins matter"):
  Abstract: This paper shows that standard assessment methodology for style transfer has several significant problems. First, the standard metrics for style accuracy and semantics preservation vary significantly on different re-runs. Therefore one has to report error margins for the obtained results. Second, starting with certain values of bilingual evaluation understudy (BLEU) between input and output and accuracy of the sentiment transfer the optimization of these two standard metrics diverge from the intuitive goal of the style transfer task. Finally, due to the nature of the task itself, there is a specific dependence between these two metrics that could be easily manipulated. Under these circumstances, we suggest taking BLEU between input and human-written reformulations into consideration for benchmarks. We also propose three new architectures that outperform state of the art in terms of this metric.

Includes members:
  Member "unsupervised paraphrasing without translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "style transfer for texts retrain report errors compare with rewrites")
  Member "delete retrieve generate a simple approach to sentiment and style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "guess who multilingual approach for the automated generation of author stylized poetry":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "style transfer for texts retrain report errors compare with rewrites")
  Member "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "structured content preservation for unsupervised text style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "imat unsupervised text attribute transfer via iterative matching and translation")
  Member "adversarial decomposition of text representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "summae zero shot abstractive text summarization using length agnostic auto encoders")
  Member "unpaired sentiment to sentiment translation a cycled reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "generating sentences by editing prototypes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue")

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)