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 "scoring sentence singletons and pairs for abstractive summarization"):
  Abstract: When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them. There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space. Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence. We conduct extensive experiments on both single- and multi-document summarization datasets and report findings on sentence selection and abstraction.

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 "content selection in deep learning models of summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "a unified model for extractive and abstractive summarization using inconsistency loss":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  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 "soft layer specific multi task summarization with entailment and question generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    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 "structure infused copy mechanisms for abstractive summarization":
    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)