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: "for the sake of simplicity unsupervised extraction of lexical simplifications from wikipedia"
  Abstract: We report on work in progress on extracting lexical simplifications (e.g., "collaborate" -> "work together"), focusing on utilizing edit histories in Simple English Wikipedia for this task. We consider two main approaches: (1) deriving simplification probabilities via an edit model that accounts for a mixture of different operations, and (2) using metadata to focus on edits that are more likely to be simplification operations. We find our methods to outperform a reasonable baseline and yield many high-quality lexical simplifications not included in an independently-created manually prepared list.

Connected through hyperedge HE-1 (induced by source paper "automatic fact guided sentence modification"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "bilinear attention networks"
    - "style transfer as unsupervised machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-2 (induced by source paper "controllable sentence simplification employing syntactic and lexical constraints"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "sentence simplification with memory augmented neural networks"
    - "quickedit editing text translations by crossing words out"
    - "attention is all you need"
    - "sentence simplification with deep reinforcement learning"
    - "controlling linguistic style aspects in neural language generation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-3 (induced by source paper "discourse level factors for sentence deletion in text simplification"):
  with members:
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "a word complexity lexicon and a neural readability ranking model for lexical simplification"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "sentence simplification with memory augmented neural networks"
    - "simple and effective text simplification using semantic and neural methods"
    - "sentence simplification with deep reinforcement learning"
    - "learning based single document summarization with compression and anaphoricity constraints"

Connected through hyperedge HE-4 (induced by source paper "causal effects of brevity on style and success in social media"):
  with members:
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "tgsum build tweet guided multi document summarization dataset"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "a computational approach to politeness with application to social factors"
    - "exploring text virality in social networks"

Connected through hyperedge HE-5 (induced by source paper "a simple bert based approach for lexical simplification"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "cross lingual language model pretraining"
    - "a word complexity lexicon and a neural readability ranking model for lexical simplification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "evaluating prose style transfer with the bible"):
  with members:
    - "unsupervised statistical machine translation"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised machine translation using monolingual corpora only"
    - "controlling linguistic style aspects in neural language generation"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"):
  with members:
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "generating sentences by editing prototypes"
    - "split and rephrase"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-8 (induced by source paper "unsupervised neural text simplification"):
  with members:
    - "style transfer as unsupervised machine translation"
    - "unsupervised statistical machine translation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "style transfer from non parallel text by cross alignment"
    - "split and rephrase"
    - "unsupervised neural machine translation"
    - "a neural attention model for abstractive sentence summarization"
    - "on the properties of neural machine translation encoder decoder approaches"

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)