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: "dynamic multi level multi task learning for sentence simplification"
  Abstract: Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its input sentence. In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a novel 'multi-level' layered soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the sentence simplification model, depending on the task's semantic versus lexico-syntactic nature. We also introduce a novel multi-armed bandit based training approach that dynamically learns how to effectively switch across tasks during multi-task learning. Experiments on multiple popular datasets demonstrate that our model outperforms competitive simplification systems in SARI and FKGL automatic metrics, and human evaluation. Further, we present several ablation analyses on alternative layer sharing methods, soft versus hard sharing, dynamic multi-armed bandit sampling approaches, and our model's learned entailment and paraphrasing skills.

Connected through hyperedge HE-1 (induced by source paper "automatic compilation of resources for academic writing and evaluating with informal word identification and paraphrasing system"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "a report on the complex word identification shared task 2018"
    - "par4sim adaptive paraphrasing for text simplification"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wronging a right generating better errors to improve grammatical error detection"
    - "learning to paraphrase for question answering"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "autosem automatic task selection and mixing in multi task learning"):
  with members:
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "automated curriculum learning for neural networks"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-3 (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-4 (induced by source paper "improving scientific article visibility by neural title simplification"):
  with members:
    - "opennmt neural machine translation toolkit"
    - "data driven summarization of scientific articles"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "joint copying and restricted generation for paraphrase"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "dynamic transfer learning for named entity recognition"):
  with members:
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "the natural language decathlon multitask learning as question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "transfer learning for sequence tagging with hierarchical recurrent 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)