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 "searching for effective neural extractive summarization what works and what s next"):
  Abstract: The recent years have seen remarkable success in the use of deep neural networks on text summarization. #R##N#However, there is no clear understanding of \textit{why} they perform so well, or \textit{how} they might be improved. #R##N#In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and #R##N#learning schemas. Additionally, we find an effective way to improve current frameworks and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our #R##N#observations and analyses. Hopefully, our work could provide more clues for future research on extractive summarization.

Includes members:
  Member "deep communicating agents for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")
  Member "newsroom a dataset of 1 3 million summaries with diverse extractive strategies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modelling latent skills for multitask language generation")
  Member "content selection in deep learning models of summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "bottom up abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")
  Member "learning to extract coherent summary via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforced self attention masks for abstractive summarization dr sas")
  Member "banditsum extractive summarization as a contextual bandit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "length controllable abstractive summarization by guiding with summary prototype")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "ranking sentences for extractive summarization with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning by semantic similarity makes abstractive summarization better")

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)