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: "a task in a suit and a tie paraphrase generation with semantic augmentation"
  Abstract: Paraphrasing is rooted in semantics. We show the effectiveness of transformers (Vaswani et al. 2017) for paraphrase generation and further improvements by incorporating PropBank labels via a multi-encoder. Evaluating on MSCOCO and WikiAnswers, we find that transformers are fast and effective, and that semantic augmentation for both transformers and LSTMs leads to sizable 2-3 point gains in BLEU, METEOR and TER. More importantly, we find surprisingly large gains on human evaluations compared to previous models. Nevertheless, manual inspection of generated paraphrases reveals ample room for improvement: even our best model produces human-acceptable paraphrases for only 28% of captions from the CHIA dataset (Sharma et al. 2018), and it fails spectacularly on sentences from Wikipedia. Overall, these results point to the potential for incorporating semantics in the task while highlighting the need for stronger evaluation.

Connected through hyperedge HE-1 (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"
    - "unsupervised paraphrasing without translation"
    - "building a conversational agent overnight with dialogue self play"
    - "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-2 (induced by source paper "decomposable neural paraphrase generation"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "learning neural templates for text generation"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "quase sequence editing under quantifiable guidance"
    - "toward controlled generation of text"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-3 (induced by source paper "polly want a cracker analyzing performance of parroting on paraphrase generation datasets"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "a continuously growing dataset of sentential paraphrases"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "microsoft coco common objects in context"

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)