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 "pegasus pre training with extracted gap sentences for abstractive summarization"):
  Abstract: Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization. However, pre-training objectives tailored for abstractive text summarization have not been explored. Furthermore there is a lack of systematic evaluation across diverse domains. In this work, we propose pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective. In PEGASUS, important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary. We evaluated our best PEGASUS model on 12 downstream summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills. Experiments demonstrate it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores. Our model also shows surprising performance on low-resource summarization, surpassing previous state-of-the-art results on 6 datasets with only 1000 examples.

Includes members:
  Member "exploring the limits of transfer learning with a unified text to text transformer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "bigpatent a large scale dataset for abstractive and coherent summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating the factual consistency of abstractive text summarization")
  Member "multi news a large scale multi document summarization dataset and abstractive hierarchical model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "summae zero shot abstractive text summarization using length agnostic auto encoders")
  Member "leafnats an open source toolkit and live demo system for neural abstractive text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modelling latent skills for multitask language generation")
  Member "sample efficient text summarization using a single pre trained transformer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fine tuning language models from human preferences")
  Member "billsum a corpus for automatic summarization of us legislation":
    also connected through other hyperedges:
      (none)
  Member "searching for effective neural extractive summarization what works and what s next":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discourse aware neural extractive model for text summarization")
  Member "spanbert improving pre training by representing and predicting spans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")

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)