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: "shaped shared private encoder decoder for text style adaptation"
  Abstract: Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing styles, such models may end up learning an 'average' style that is directly influenced by the training data make-up and cannot be controlled by the needs of an application. We describe a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters. Such models are able to generate language according to a specific learned style, while still taking advantage of their power to model generic language phenomena. Furthermore, we describe an extension that uses a mixture of output distributions from all learned styles to perform on-the fly style adaptation based on the textual input alone. Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities.

Connected through hyperedge HE-1 (induced by source paper "domain adaptive text style transfer"):
  with members:
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "consistent dialogue generation with self supervised feature learning"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning based text style transfer without parallel training corpus"):
  with members:
    - "improving reinforcement learning based image captioning with natural language prior"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "a study of reinforcement learning for neural machine translation"
    - "style transfer through back translation"
    - "polite dialogue generation without parallel data"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "word mover s embedding from word2vec to document embedding"

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"
    - "multi task neural models for translating between styles within and across languages"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "rethinking text attribute transfer a lexical analysis"):
  with members:
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "a dual reinforcement learning framework for unsupervised text style transfer"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"

Connected through hyperedge HE-5 (induced by source paper "generating summaries tailored to target characteristics"):
  with members:
    - "multi task neural models for translating between styles within and across languages"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "style transfer through back 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"
    - "controlling linguistic style aspects in neural language generation"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "style transformer unpaired text style transfer without disentangled latent representation"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "imat unsupervised text attribute transfer via iterative matching and translation"
    - "style transfer as unsupervised machine translation"
    - "disentangled representation learning for non parallel text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "transforming delete retrieve generate approach for controlled text style transfer"):
  with members:
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "evaluating style transfer for text"
    - "style transfer as unsupervised machine translation"
    - "disentangled representation learning for non parallel text style transfer"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"

Connected through hyperedge HE-8 (induced by source paper "dpugc learn differentially private representation for user generated contents"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "detecting communities under differential privacy"
    - "enriching word vectors with subword information"
    - "nonparametric spherical topic modeling with word embeddings"
    - "document classification by inversion of distributed language representations"
    - "efficient estimation of word representations in vector space"

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)