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: "a constrained sequence to sequence neural model for sentence simplification"
  Abstract: Sentence simplification reduces semantic complexity to benefit people with language impairments. Previous simplification studies on the sentence level and word level have achieved promising results but also meet great challenges. For sentence-level studies, sentences after simplification are fluent but sometimes are not really simplified. For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification. In this paper, we propose a two-step simplification framework by combining both the word-level and the sentence-level simplifications, making use of their corresponding advantages. Based on the two-step framework, we implement a novel constrained neural generation model to simplify sentences given simplified words. The final results on Wikipedia and Simple Wikipedia aligned datasets indicate that our method yields better performance than various baselines.

Connected through hyperedge HE-1 (induced by source paper "open information extraction from question answer pairs"):
  with members:
    - "transforming question answering datasets into natural language inference datasets"
    - "neural open information extraction"
    - "opennmt open source toolkit for neural machine translation"
    - "indirect supervision for relation extraction using question answer pairs"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "learning a neural semantic parser from user feedback"
    - "a syntactic neural model for general purpose code generation"
    - "recurrent neural machine translation"

Connected through hyperedge HE-2 (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"
    - "simple and effective text simplification using semantic and neural methods"
    - "sentence simplification with deep reinforcement learning"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "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-3 (induced by source paper "simple and effective text simplification using semantic and neural methods"):
  with members:
    - "a transition based directed acyclic graph parser for ucca"
    - "split and rephrase"
    - "opennmt open source toolkit for neural machine translation"
    - "hume human ucca based evaluation of machine translation"
    - "effective approaches to attention based neural machine translation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "bleu is not suitable for the evaluation of text simplification"):
  with members:
    - "simple and effective text simplification using semantic and neural methods"
    - "split and rephrase"
    - "a semantic relevance based neural network for text summarization and text 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)