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: "unsupervised paraphrasing without translation"
  Abstract: Paraphrasing exemplifies the ability to abstract semantic content from surface forms. Recent work on automatic paraphrasing is dominated by methods leveraging Machine Translation (MT) as an intermediate step. This contrasts with humans, who can paraphrase without being bilingual. This work proposes to learn paraphrasing models from an unlabeled monolingual corpus only. To that end, we propose a residual variant of vector-quantized variational auto-encoder. #R##N#We compare with MT-based approaches on paraphrase identification, generation, and training augmentation. Monolingual paraphrasing outperforms unsupervised translation in all settings. Comparisons with supervised translation are more mixed: monolingual paraphrasing is interesting for identification and augmentation; supervised translation is superior for generation.

Connected through hyperedge HE-1 (induced by source paper "style transfer for texts retrain report errors compare with rewrites"):
  with members:
    - "style transfer as unsupervised machine translation"
    - "evaluating prose style transfer with the bible"
    - "multiple attribute text style transfer"
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"
    - "guess who multilingual approach for the automated generation of author stylized poetry"

Connected through hyperedge HE-2 (induced by source paper "unsupervised paraphrasing by simulated annealing"):
  with members:
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "decomposable neural paraphrase generation"
    - "building a conversational agent overnight with dialogue self play"
    - "a task in a suit and a tie paraphrase generation with semantic augmentation"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "generating sentences by editing prototypes"
    - "a continuously growing dataset of sentential paraphrases"

Connected through hyperedge HE-3 (induced by source paper "style transfer for texts to err is human but error margins matter"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "guess who multilingual approach for the automated generation of author stylized poetry"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "structured content preservation for unsupervised text style transfer"
    - "adversarial decomposition of text representation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "generating sentences by editing prototypes"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-4 (induced by source paper "paraphrasing with large language models"):
  with members:
    - "unified language model pre training for natural language understanding and generation"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "a deep generative framework for paraphrase generation"

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.LG(Machine Learning)