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 "effective writing style imitation via combinatorial paraphrasing"):
  Abstract: Stylometry can be used to profile authors based on their written text. Transforming text to imitate someone else's writing style while retaining meaning constitutes a defence. A variety of deep learning methods for style imitation have been proposed in recent research literature. Via empirical evaluation of three state-of-the-art models on four datasets, we illustrate that none succeed in semantic retainment, often drastically changing the original meaning or removing important parts of the text. To mitigate this problem we present ParChoice: an alternative approach based on the combinatorial application of multiple paraphrasing techniques. ParChoice first produces a large number of possible candidate paraphrases, from which it then chooses the candidate that maximizes proximity to a target corpus. Through systematic automated and manual evaluation as well as a user study, we demonstrate that ParChoice significantly outperforms prior methods in its ability to retain semantic content. Using state-of-the art deep learning author profiling tools, we additionally show that ParChoice accomplishes better imitation success than A$^4$NT, the state-of-the-art style imitation technique with the best semantic retainment.

Includes members:
  Member "eat2seq a generic framework for controlled sentence transformation without task specific training":
    also connected through other hyperedges:
      (none)
  Member "style transfer through back translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "content preserving text generation with attribute controls":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "imat unsupervised text attribute transfer via iterative matching and translation")
  Member "style transfer from non parallel text by cross alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "recent trends in deep learning based natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "paraphrase generation with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "paraphrase generation with latent bag of words")
  Member "achieving open vocabulary neural machine translation with hybrid word character models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "authorship attribution in bangla literature using character level cnn")
  Member "domain specific author attribution based on feedforward neural network language models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace")

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)