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: "split and rephrase"
  Abstract: We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Because shorter sentences are generally better processed by NLP systems, it could be used as a preprocessing step which facilitates and improves the performance of parsers, semantic role labellers and machine translation systems. It should also be of use for people with reading disabilities because it allows the conversion of longer sentences into shorter ones. This paper makes two contributions towards this new task. First, we create and make available a benchmark consisting of 1,066,115 tuples mapping a single complex sentence to a sequence of sentences expressing the same meaning. Second, we propose five models (vanilla sequence-to-sequence to semantically-motivated models) to understand the difficulty of the proposed task.

Connected through hyperedge HE-1 (induced by source paper "fact aware sentence split and rephrase with permutation invariant training"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "split and rephrase better evaluation and a stronger baseline"
    - "learning to split and rephrase from wikipedia edit history"
    - "deep attractor network for single microphone speaker separation"
    - "attention is all you need"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "joint parsing and generation for abstractive summarization"):
  with members:
    - "analyzing sentence fusion in abstractive summarization"
    - "controlling the amount of verbatim copying in abstractive summarization"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "automatic fact guided sentence modification"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "bilinear attention networks"
    - "style transfer as unsupervised machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-4 (induced by source paper "transforming complex sentences into a semantic hierarchy"):
  with members:
    - "semantic structural evaluation for text simplification"
    - "graphene semantically linked propositions in open information extraction"
    - "bleu is not suitable for the evaluation of text simplification"
    - "simple and effective text simplification using semantic and neural methods"
    - "learning to split and rephrase from wikipedia edit history"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "discofuse a large scale dataset for discourse based sentence fusion"):
  with members:
    - "training tips for the transformer model"
    - "learning to split and rephrase from wikipedia edit history"
    - "attention is all you need"
    - "automatic prediction of discourse connectives"
    - "implicit discourse relation classification via multi task neural networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural machine translation of rare words with subword units"
    - "abstractive multi document summarization via phrase selection and merging"

Connected through hyperedge HE-6 (induced by source paper "minwikisplit a sentence splitting corpus with minimal propositions"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "semantic structural evaluation for text simplification"
    - "graphene semantically linked propositions in open information extraction"
    - "bleu is not suitable for the evaluation of text simplification"
    - "neural network acceptability judgments"
    - "learning to split and rephrase from wikipedia edit history"

Connected through hyperedge HE-7 (induced by source paper "reference less quality estimation of text simplification systems"):
  with members:
    - "advances in pre training distributed word representations"
    - "convolutional sequence to sequence learning"
    - "text readability assessment for second language learners"
    - "skip thought vectors"

Connected through hyperedge HE-8 (induced by source paper "dissim a discourse aware syntactic text simplification frameworkfor english and german"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "a survey on open information extraction"

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)