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 sentence simplification using deep semantics"
  Abstract: We present a novel approach to sentence simplification which departs from previous work in two main ways. First, it requires neither hand written rules nor a training corpus of aligned standard and simplified sentences. Second, sentence splitting operates on deep semantic structure. We show (i) that the unsupervised framework we propose is competitive with four state-of-the-art supervised systems and (ii) that our semantic based approach allows for a principled and effective handling of sentence splitting.

Connected through hyperedge HE-1 (induced by source paper "discourse level factors for sentence deletion in text simplification"):
  with members:
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "a word complexity lexicon and a neural readability ranking model for lexical simplification"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "sentence simplification with memory augmented neural networks"
    - "simple and effective text simplification using semantic and neural methods"
    - "sentence simplification with deep reinforcement learning"
    - "learning based single document summarization with compression and anaphoricity constraints"

Connected through hyperedge HE-2 (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"
    - "split and rephrase"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-3 (induced by source paper "plain english summarization of contracts"):
  with members:
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "unsupervised text style transfer using language models as discriminators"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "style transfer from non parallel text by cross alignment"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "inherent biases in reference based evaluation for grammatical error correction and text simplification"):
  with members:
    - "automatic metric validation for grammatical error correction"
    - "sentence simplification with deep reinforcement learning"
    - "phrase based machine translation is state of the art for automatic grammatical error correction"
    - "neural language correction with character based attention"
    - "there s no comparison reference less evaluation metrics in grammatical error correction"
    - "more accurate tests for the statistical significance of result differences"

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)