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 "controlling the amount of verbatim copying in abstractive summarization"):
  Abstract: An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstraction. Human editors can usually exercise control over copying, resulting in summaries that are more extractive than abstractive, or vice versa. However, it remains poorly understood whether modern neural abstractive summarizers can provide the same flexibility, i.e., learning from single reference summaries to generate multiple summary hypotheses with varying degrees of copying. In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones. We frame the task of summarization as language modeling and exploit alternative mechanisms to generate summary hypotheses. Our method allows for control over copying during both training and decoding stages of a neural summarization model. Through extensive experiments we illustrate the significance of our proposed method on controlling the amount of verbatim copying and achieve competitive results over strong baselines. Our analysis further reveals interesting and unobvious facts.

Includes members:
  Member "sample efficient text summarization using a single pre trained transformer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pegasus pre training with extracted gap sentences for abstractive summarization")
  Member "biset bi directional selective encoding with template for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attend to the beginning a study on using bidirectional attention for extractive summarization")
  Member "bertscore evaluating text generation with bert":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on feature normalization and data augmentation")
  Member "unified language model pre training for natural language understanding and generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "deep communicating agents for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")
  Member "improving abstraction in text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "avgout a simple output probability measure to eliminate dull responses")
  Member "newsroom a dataset of 1 3 million summaries with diverse extractive strategies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modelling latent skills for multitask language generation")
  Member "bottom up abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")

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)