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: "learning to paraphrase an unsupervised approach using multiple sequence alignment"
  Abstract: We address the text-to-text generation problem of sentence-level paraphrasing -- a phenomenon distinct from and more difficult than word- or phrase-level paraphrasing. Our approach applies multiple-sequence alignment to sentences gathered from unannotated comparable corpora: it learns a set of paraphrasing patterns represented by word lattice pairs and automatically determines how to apply these patterns to rewrite new sentences. The results of our evaluation experiments show that the system derives accurate paraphrases, outperforming baseline systems.

Connected through hyperedge HE-1 (induced by source paper "smart summarizer for blind people"):
  with members:
    - "high accurate unhealthy leaf detection"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-2 (induced by source paper "an empirical comparison on imitation learning and reinforcement learning for paraphrase generation"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "learning semantic sentence embeddings using pair wise discriminator"
    - "the natural language decathlon multitask learning as question answering"
    - "a study of reinforcement learning for neural machine translation"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "a continuously growing dataset of sentential paraphrases"

Connected through hyperedge HE-3 (induced by source paper "unsupervised paraphrasing without translation"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "fast decoding in sequence models using discrete latent variables"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "theory and experiments on vector quantized autoencoders"
    - "quickedit editing text translations by crossing words out"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "decomposable neural paraphrase generation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "label dependent deep variational paraphrase generation"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural summarization by extracting sentences and words"
    - "diverse beam search decoding diverse solutions from neural sequence models"

Connected through hyperedge HE-7 (induced by source paper "essentia mining domain specific paraphrases with word alignment graphs"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "a deep generative framework for paraphrase generation"

Connected through hyperedge HE-8 (induced by source paper "a neural multi sequence alignment technique neumatch"):
  with members:
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "learning transferable architectures for scalable image recognition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "dense captioning events in videos"
    - "jointly modeling embedding and translation to bridge video and language"
    - "skip thought vectors"
    - "a dataset for movie description"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"

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)