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 obfuscation by invariance"):
  Abstract: The task of obfuscating writing style using sequence models has previously been investigated under the framework of obfuscation-by-transfer, where the input text is explicitly rewritten in another style. These approaches also often lead to major alterations to the semantic content of the input. In this work, we propose obfuscation-by-invariance, and investigate to what extent models trained to be explicitly style-invariant preserve semantics. We evaluate our architectures on parallel and non-parallel corpora, and compare automatic and human evaluations on the obfuscated sentences. Our experiments show that style classifier performance can be reduced to chance level, whilst the automatic evaluation of the output is seemingly equal to models applying style-transfer. However, based on human evaluation we demonstrate a trade-off between the level of obfuscation and the observed quality of the output in terms of meaning preservation and grammaticality.

Includes members:
  Member "when coding style survives compilation de anonymizing programmers from executable binaries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parallelizing binary code analysis")
  Member "cross lingual distillation for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification")
  Member "the case for being average a mediocrity approach to style masking and author obfuscation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace")
  Member "a 4 nt author attribute anonymity by adversarial training of neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimization guided binary diversification to mislead neural networks for malware detection")
  Member "show adapt and tell adversarial training of cross domain image captioner":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "better captioning with sequence level exploration")
  Member "controlling linguistic style aspects in neural language generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational template machine for data to text generation")
  Member "controllable invariance through adversarial feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "adversarial multi task learning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")

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)