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: "an experimental study of lstm encoder decoder model for text simplification"
  Abstract: Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning. Current automatic TS techniques are limited to either lexical-level applications or manually defining a large amount of rules. Since deep neural networks are powerful models that have achieved excellent performance over many difficult tasks, in this paper, we propose to use the Long Short-Term Memory (LSTM) Encoder-Decoder model for sentence level TS, which makes minimal assumptions about word sequence. We conduct preliminary experiments to find that the model is able to learn operation rules such as reversing, sorting and replacing from sequence pairs, which shows that the model may potentially discover and apply rules such as modifying sentence structure, substituting words, and removing words for TS.

Connected through hyperedge HE-1 (induced by source paper "evaluating prose style transfer with the bible"):
  with members:
    - "unsupervised statistical machine translation"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised machine translation using monolingual corpora only"
    - "controlling linguistic style aspects in neural language generation"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "discourse embellishment using a deep encoder decoder network"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "effective approaches to attention based neural machine translation"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "neural machine translation by jointly learning to align and translate"
    - "recurrent neural network regularization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "improving neural text simplification model with simplified corpora"):
  with members:
    - "sttm a tool for short text topic modeling"
    - "sentence simplification with deep reinforcement learning"
    - "improving neural machine translation models with monolingual data"
    - "a neural attention model for abstractive sentence summarization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

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)