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: "decomposable neural paraphrase generation"
  Abstract: Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence at different levels of granularity in a disentangled way. Specifically, the model is composed of multiple encoders and decoders with different structures, each of which corresponds to a specific granularity. The empirical study shows that the decomposition mechanism of DNPG makes paraphrase generation more interpretable and controllable. Based on DNPG, we further develop an unsupervised domain adaptation method for paraphrase generation. Experimental results show that the proposed model achieves competitive in-domain performance compared to the state-of-the-art neural models, and significantly better performance when adapting to a new domain.

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"
    - "unsupervised paraphrasing without translation"
    - "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-2 (induced by source paper "zero shot paraphrase generation with multilingual language models"):
  with members:
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "mass masked sequence to sequence pre training for language generation"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "the missing ingredient in zero shot neural machine translation"
    - "generating wikipedia by summarizing long sequences"
    - "unsupervised natural language generation with denoising autoencoders"

Connected through hyperedge HE-3 (induced by source paper "how to ask better questions a large scale multi domain dataset for rewriting ill formed questions"):
  with members:
    - "translating web search queries into natural language questions"
    - "corpora generation for grammatical error correction"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "identifying well formed natural language questions"
    - "learning to paraphrase for question answering"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "ask the right questions active question reformulation with reinforcement learning"

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)