human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "personalized machine translation preserving original author traits"
  Abstract: The language that we produce reflects our personality, and various personal and demographic characteristics can be detected in natural language texts. We focus on one particular personal trait of the author, gender, and study how it is manifested in original texts and in translations. We show that author's gender has a powerful, clear signal in originals texts, but this signal is obfuscated in human and machine translation. We then propose simple domain-adaptation techniques that help retain the original gender traits in the translation, without harming the quality of the translation, thereby creating more personalized machine translation systems.

Connected through hyperedge HE-1 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace"):
  with members:
    - "hunting for troll comments in news community forums"
    - "all you need is love evading hate speech detection"
    - "a survey on natural language processing for fake news detection"
    - "style transfer through back translation"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "stay on topic generating context specific fake restaurant reviews"

Connected through hyperedge HE-2 (induced by source paper "controlling neural machine translation formality with synthetic supervision"):
  with members:
    - "consistency by agreement in zero shot neural machine translation"
    - "multi task neural models for translating between styles within and across languages"
    - "the missing ingredient in zero shot neural machine translation"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "identifying semantic divergences in parallel text without annotations"
    - "contextual parameter generation for universal neural machine translation"
    - "extreme adaptation for personalized neural machine translation"
    - "monolingual and cross lingual zero shot style transfer"

Connected through hyperedge HE-3 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation"):
  with members:
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "detecting and explaining causes from text for a time series event"
    - "automated hate speech detection and the problem of offensive language"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-4 (induced by source paper "controlling text complexity in neural machine translation"):
  with members:
    - "multi task neural models for translating between styles within and across languages"
    - "getting gender right in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "sentence simplification with deep reinforcement learning"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "filling gender number gaps in neural machine translation with black box context injection"):
  with members:
    - "context aware neural machine translation learns anaphora resolution"
    - "neural machine translation with extended context"
    - "evaluating discourse phenomena in neural machine translation"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-6 (induced by source paper "style transfer as unsupervised machine translation"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "phrase based neural unsupervised machine translation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "multi task neural models for translating between styles within and across languages"):
  with members:
    - "style transfer through back translation"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "identifying semantic divergences in parallel text without annotations"
    - "extreme adaptation for personalized neural machine translation"
    - "bi directional neural machine translation with synthetic parallel data"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "sockeye a toolkit for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "style transfer through back translation"):
  with members:
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised machine translation using monolingual corpora only"
    - "learning to paraphrase for question answering"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "unsupervised neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "style transfer in text exploration and evaluation"

Question: Please tell me which class the center node 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)