human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "pre trained language model representations for language generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "defending against neural fake news"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "modelling latent skills for multitask language generation"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "guiding extractive summarization with question answering rewards"
    - "continual unsupervised representation learning"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "putting machine translation in context with the noisy channel model"
    - "neural extractive text summarization with syntactic compression"
    - "text summarization with pretrained encoders"
    - "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"

Connected through hyperedge HE-3 (induced by source paper "ernie gen an enhanced multi flow pre training and fine tuning framework for natural language generation"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "plato pre trained dialogue generation model with discrete latent variable"
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "text summarization with pretrained encoders"

Connected through hyperedge HE-4 (induced by source paper "learning by semantic similarity makes abstractive summarization better"):
  with members:
    - "prophetnet predicting future n gram for sequence to sequence pre training"
    - "summary level training of sentence rewriting for abstractive summarization"
    - "text summarization with pretrained encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"
    - "better rewards yield better summaries learning to summarise without references"
    - "bottom up abstractive summarization"
    - "adversarial text generation via feature mover s distance"

Connected through hyperedge HE-5 (induced by source paper "make lead bias in your favor a simple and effective method for news summarization"):
  with members:
    - "importance of copying mechanism for news headline generation"
    - "passage re ranking with bert"
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "on the variance of the adaptive learning rate and beyond"
    - "bottlesum unsupervised and self supervised sentence summarization using the information bottleneck principle"
    - "text summarization with pretrained encoders"
    - "unified language model pre training for natural language understanding and generation"
    - "content selection in deep learning models of summarization"

Question: Please tell me which class the center node 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)