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 "an editorial network for enhanced document summarization"):
  Abstract: We suggest a new idea of Editorial Network - a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences. Our network tries to imitate the decision process of a human editor during summarization. Within such a process, each extracted sentence may be either kept untouched, rephrased or completely rejected. We further suggest an effective way for training the "editor" based on a novel soft-labeling approach. Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractive-only baseline methods.

Includes members:
  Member "a unified model for extractive and abstractive summarization using inconsistency loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "generating wikipedia by summarizing long sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "learning to extract coherent summary via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reinforced self attention masks for abstractive summarization dr sas")
  Member "multi reward reinforced summarization with saliency and entailment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generating representative headlines for news stories")
  Member "a discourse aware attention model for abstractive summarization of long documents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning contextualized document representations for healthcare answer retrieval")
  Member "closed book training to improve summarization encoder memory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural text summarization a critical evaluation")
  Member "neural document summarization by jointly learning to score and select sentences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "selective attention encoders by syntactic graph convolutional networks for document summarization")
  Member "neural latent extractive document summarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generating representative headlines for news stories")

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)