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: "simple and effective text simplification using semantic and neural methods"
  Abstract: Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.

Connected through hyperedge HE-1 (induced by source paper "made for each other broad coverage semantic structures meet preposition supersenses"):
  with members:
    - "semeval 2019 task 1 cross lingual semantic parsing with ucca"
    - "on the convergence of adam and beyond"
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "semantically constrained multilayer annotation the case of coreference"
    - "linguistic knowledge and transferability of contextual representations"
    - "semantic structural evaluation for text simplification"
    - "embedding syntax and semantics of prepositions via tensor decomposition"
    - "syntactic scaffolds for semantic structures"

Connected through hyperedge HE-2 (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"
    - "sentence simplification with memory augmented neural networks"
    - "sentence simplification with deep reinforcement learning"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "unsupervised sentence simplification using deep semantics"

Connected through hyperedge HE-3 (induced by source paper "semeval 2019 task 1 cross lingual semantic parsing with ucca"):
  with members:
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "toward abstractive summarization using semantic representations"
    - "multitask parsing across semantic representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a transition based directed acyclic graph parser for ucca"
    - "a minimal span based neural constituency parser"
    - "text summarization using abstract meaning representation"

Connected through hyperedge HE-4 (induced by source paper "transforming complex sentences into a semantic hierarchy"):
  with members:
    - "semantic structural evaluation for text simplification"
    - "graphene semantically linked propositions in open information extraction"
    - "bleu is not suitable for the evaluation of text simplification"
    - "learning to split and rephrase from wikipedia edit history"
    - "split and rephrase"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "editnts an neural programmer interpreter model for sentence simplification through explicit editing"):
  with members:
    - "bleu is not suitable for the evaluation of text simplification"
    - "sentence simplification with deep reinforcement learning"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "a constrained sequence to sequence neural model for sentence simplification"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "deep residual learning for image recognition"
    - "neural programmer interpreters"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "content differences in syntactic and semantic representations"):
  with members:
    - "syntactic scaffolds for semantic structures"
    - "universal dependency parsing with a general transition based dag parser"
    - "multitask parsing across semantic representations"
    - "linguistically informed self attention for semantic role labeling"
    - "a transition based directed acyclic graph parser for ucca"
    - "hume human ucca based evaluation of machine translation"
    - "an incremental parser for abstract meaning representation"
    - "getting more out of syntax with props"

Connected through hyperedge HE-7 (induced by source paper "bleu is not suitable for the evaluation of text simplification"):
  with members:
    - "split and rephrase"
    - "a semantic relevance based neural network for text summarization and text simplification"
    - "a constrained sequence to sequence neural model for sentence simplification"

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)