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: "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
  Abstract: We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach. Most current neural sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. These methods learn to simplify sentences as a byproduct of the fact that they are trained on complex-simple sentence pairs. By contrast, our neural programmer-interpreter is directly trained to predict explicit edit operations on targeted parts of the input sentence, resembling the way that humans might perform simplification and revision. Our model outperforms previous state-of-the-art neural sentence simplification models (without external knowledge) by large margins on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, +1.41 Newsela), and is judged by humans to produce overall better and simpler output sentences.

Connected through hyperedge HE-1 (induced by source paper "insertion deletion transformer"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "non monotonic sequential text generation"
    - "levenshtein transformer"
    - "kermit generative insertion based modeling for sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "discourse aware neural extractive model for text summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "fine tune bert for extractive summarization"
    - "searching for effective neural extractive summarization what works and what s next"
    - "an entity driven framework for abstractive summarization"
    - "neural extractive text summarization with syntactic compression"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"
    - "content selection in deep learning models of summarization"

Connected through hyperedge HE-3 (induced by source paper "unsupervised paraphrasing by simulated annealing"):
  with members:
    - "decomposable neural paraphrase generation"
    - "unsupervised paraphrasing without translation"
    - "building a conversational agent overnight with dialogue self play"
    - "a task in a suit and a tie paraphrase generation with semantic augmentation"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "generating sentences by editing prototypes"
    - "a continuously growing dataset of sentential paraphrases"

Connected through hyperedge HE-4 (induced by source paper "encode tag realize high precision text editing"):
  with members:
    - "fine tune bert for extractive summarization"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "imat unsupervised text attribute transfer via iterative matching and translation"
    - "step by step separating planning from realization in neural data to text generation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "learning neural templates for text generation"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "levenshtein transformer"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "text repair model for neural machine translation"
    - "non monotonic sequential text generation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "optimal completion distillation for sequence learning"

Connected through hyperedge HE-6 (induced by source paper "discourse level factors for sentence deletion in text simplification"):
  with members:
    - "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"
    - "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)