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 deep reinforcement learning"
  Abstract: Sentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and simple sentences. We address the simplification problem with an encoder-decoder model coupled with a deep reinforcement learning framework. Our model, which we call {\sc Dress} (as shorthand for {\bf D}eep {\bf RE}inforcement {\bf S}entence {\bf S}implification), explores the space of possible simplifications while learning to optimize a reward function that encourages outputs which are simple, fluent, and preserve the meaning of the input. Experiments on three datasets demonstrate that our model outperforms competitive simplification systems.

Connected through hyperedge HE-1 (induced by source paper "generative question refinement with deep reinforcement learning in retrieval based qa system"):
  with members:
    - "learning to coordinate multiple reinforcement learning agents for diverse query reformulation"
    - "proximal policy optimization and its dynamic version for sequence generation"
    - "identifying well formed natural language questions"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "learning to paraphrase for question answering"
    - "ask the right questions active question reformulation with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "this email could save your life introducing the task of email subject line generation"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "metric for automatic machine translation evaluation based on universal sentence representations"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "actionable email intent modeling with reparametrized rnns"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning based text style transfer without parallel training corpus"):
  with members:
    - "improving reinforcement learning based image captioning with natural language prior"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "a study of reinforcement learning for neural machine translation"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "polite dialogue generation without parallel data"
    - "graph2seq graph to sequence learning with attention based neural networks"

Connected through hyperedge HE-4 (induced by source paper "text as environment a deep reinforcement learning text readability assessment model"):
  with members:
    - "reference less quality estimation of text simplification systems"
    - "learning when not to answer a ternary reward structure for reinforcement learning based question answering"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "towards diverse text generation with inverse reinforcement learning"
    - "a machine learning approach to persian text readability assessment using a crowdsourced dataset"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "quality of syntactic implication of rl based sentence summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "multi reward reinforced summarization with saliency and entailment"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"
    - "modeling source syntax for neural machine translation"
    - "faithful to the original fact aware neural abstractive summarization"

Connected through hyperedge HE-6 (induced by source paper "rl lim reinforcement learning based locally interpretable modeling"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "regularizing black box models for improved interpretability"
    - "model agnostic supervised local explanations"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning to reweight examples for robust deep learning"
    - "on the robustness of interpretability methods"
    - "online and linear time attention by enforcing monotonic alignments"
    - "interpretable explorable approximations of black box models"

Connected through hyperedge HE-7 (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"
    - "sentence simplification with memory augmented neural networks"

Connected through hyperedge HE-8 (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"
    - "sentence simplification with memory augmented neural networks"
    - "quickedit editing text translations by crossing words out"
    - "attention is all you need"
    - "controlling linguistic style aspects in neural language generation"
    - "get to the point summarization with pointer generator networks"
    - "controllable abstractive summarization"

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)