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: "multi task neural models for translating between styles within and across languages"
  Abstract: Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine translation. We propose to solve these tasks jointly using multi-task learning, and show that our models achieve state-of-the-art performance for formality transfer and are able to perform formality-sensitive translation without being explicitly trained on style-annotated translation examples.

Connected through hyperedge HE-1 (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting"):
  with members:
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "investigating backtranslation in neural machine translation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "style transfer through back translation"

Connected through hyperedge HE-2 (induced by source paper "controlling the output length of neural machine translation"):
  with members:
    - "positional encoding to control output sequence length"
    - "revisiting character based neural machine translation with capacity and compression"
    - "achieving human parity on automatic chinese to english news translation"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "scaling neural machine translation"
    - "neural machine translation into language varieties"

Connected through hyperedge HE-3 (induced by source paper "grammatical error correction and style transfer via zero shot monolingual translation"):
  with members:
    - "neural grammatical error correction with finite state transducers"
    - "unsupervised text style transfer using language models as discriminators"
    - "style transfer as unsupervised machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "disentangled representation learning for non parallel text style transfer"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"

Connected through hyperedge HE-4 (induced by source paper "generating summaries tailored to target characteristics"):
  with members:
    - "controlling personality based stylistic variation with neural natural language generators"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "controlling linguistic style aspects in neural language generation"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "controlling neural machine translation formality with synthetic supervision"):
  with members:
    - "consistency by agreement in zero shot neural machine translation"
    - "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"
    - "sockeye a toolkit for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "controlling text complexity in neural machine translation"):
  with members:
    - "getting gender right in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "sentence simplification with deep reinforcement learning"
    - "personalized machine translation preserving original author traits"
    - "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"

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)