human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "discourse level factors for sentence deletion in text simplification"):
  Abstract: This paper presents a data-driven study focusing on analyzing and predicting sentence deletion --- a prevalent but understudied phenomenon in document simplification --- on a large English text simplification corpus. We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected. We reveal that professional editors utilize different strategies to meet readability standards of elementary and middle schools. To predict whether a sentence will be deleted during simplification to a certain level, we harness automatically aligned data to train a classification model. Evaluated on our manually annotated data, our best models reached F1 scores of 65.2 and 59.7 for this task at the levels of elementary and middle school, respectively. We find that discourse level factors contribute to the challenging task of predicting sentence deletion for simplification.

Includes members:
  Member "editnts an neural programmer interpreter model for sentence simplification through explicit editing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "insertion deletion transformer")
  Member "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "a word complexity lexicon and a neural readability ranking model for lexical simplification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "twitter watch leveraging social media to monitor and predict collective efficacy of neighborhoods")
  Member "unsupervised learning of sentence embeddings using compositional n gram features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "sentence simplification with memory augmented neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "controllable sentence simplification")
  Member "simple and effective text simplification using semantic and neural methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "made for each other broad coverage semantic structures meet preposition supersenses")
  Member "sentence simplification with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative question refinement with deep reinforcement learning in retrieval based qa system")
  Member "learning based single document summarization with compression and anaphoricity constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")

Question: Please tell me which class the source paper 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)