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 aware neural model with application in authorship attribution"):
  Abstract: Writing style is a combination of consistent decisions associated with a specific author at different levels of language production, including lexical, syntactic, and structural. In this paper, we introduce a style-aware neural model to encode document information from three stylistic levels and evaluate it in the domain of authorship attribution. First, we propose a simple way to jointly encode syntactic and lexical representations of sentences. Subsequently, we employ an attention-based hierarchical neural network to encode the syntactic and semantic structure of sentences in documents while rewarding the sentences which contribute more to capturing the writing style. Our experimental results, based on four benchmark datasets, reveal the benefits of encoding document information from all three stylistic levels when compared to the baseline methods in the literature.

Includes members:
  Member "experiments with neural networks for small and large scale authorship verification":
    also connected through other hyperedges:
      (none)
  Member "the importance of being recurrent for modeling hierarchical structure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "deep rnns encode soft hierarchical syntax":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "liar liar pants on fire a new benchmark dataset for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "the effect of different writing tasks on linguistic style a case study of the roc story cloze task":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  Member "authorship clustering using multi headed recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "syntactic recurrent neural network for authorship attribution")
  Member "authorship attribution using a neural network language model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning invariant representations of social media users")
  Member "character level and multi channel convolutional neural networks for large scale authorship attribution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "authorship attribution in bangla literature using character level cnn")

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)