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: "sentence simplification with memory augmented neural networks"
  Abstract: Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in neural machine translation have paved the way for novel approaches to the task. In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.

Connected through hyperedge HE-1 (induced by source paper "controllable sentence simplification"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "reference less quality estimation of text simplification systems"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "easse easier automatic sentence simplification evaluation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "unsupervised sentence compression using denoising auto encoders"
    - "bleu is not suitable for the evaluation of text simplification"
    - "dynamic multi level multi task learning for sentence simplification"

Connected through hyperedge HE-2 (induced by source paper "controllable sentence simplification employing syntactic and lexical constraints"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "quickedit editing text translations by crossing words out"
    - "attention is all you need"
    - "sentence simplification with deep reinforcement learning"
    - "controlling linguistic style aspects in neural language generation"
    - "get to the point summarization with pointer generator networks"
    - "controllable abstractive summarization"

Connected through hyperedge HE-3 (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"
    - "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"
    - "unsupervised sentence simplification using deep semantics"

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)