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: "leveraging pre trained checkpoints for sequence generation tasks"
  Abstract: Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. Warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we present an extensive empirical study on the utility of initializing large Transformer-based sequence-to-sequence models with the publicly available pre-trained BERT and GPT-2 checkpoints for sequence generation. We have run over 300 experiments spending thousands of TPU hours to find the recipe that works best and demonstrate that it results in new state-of-the-art results on Machine Translation, Summarization, Sentence Splitting and Sentence Fusion.

Connected through hyperedge HE-1 (induced by source paper "xpersona evaluating multilingual personalized chatbot"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "towards a human like open domain chatbot"
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "code switched language models using neural based synthetic data from parallel sentences"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "zero shot cross lingual dialogue systems with transferable latent variables"

Connected through hyperedge HE-2 (induced by source paper "generating representative headlines for news stories"):
  with members:
    - "highres highlight based reference less evaluation of summarization"
    - "ernie enhanced representation through knowledge integration"
    - "facet aware evaluation for extractive text summarization"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "ernie enhanced language representation with informative entities"
    - "self attentive model for headline generation"
    - "roberta a robustly optimized bert pretraining approach"

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"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "pegasus pre training with extracted gap sentences for abstractive summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "sample efficient text summarization using a single pre trained transformer"
    - "billsum a corpus for automatic summarization of us legislation"
    - "searching for effective neural extractive summarization what works and what s next"
    - "spanbert improving pre training by representing and predicting spans"

Connected through hyperedge HE-5 (induced by source paper "sticking to the facts confident decoding for faithful data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "data to text generation with entity modeling"
    - "text generation with exemplar based adaptive decoding"
    - "handling divergent reference texts when evaluating table to text generation"
    - "calibration entropy rates and memory in language models"
    - "bottom up abstractive summarization"
    - "learning neural templates for text generation"

Connected through hyperedge HE-6 (induced by source paper "multi stage pretraining for abstractive summarization"):
  with members:
    - "the curious case of neural text degeneration"
    - "pretraining based natural language generation for text summarization"
    - "neural text generation with unlikelihood training"
    - "rethinking imagenet pre training"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "entity commonsense representation for neural abstractive summarization"
    - "actor critic based training framework for abstractive summarization"

Connected through hyperedge HE-7 (induced by source paper "text summarization with pretrained encoders"):
  with members:
    - "pre trained language model representations for language generation"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"
    - "bottom up abstractive summarization"
    - "banditsum extractive summarization as a contextual bandit"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "unsupervised pre training for natural language generation a literature review"):
  with members:
    - "language models as knowledge bases"
    - "denoising based sequence to sequence pre training for text generation"
    - "pre trained language model representations for language generation"
    - "do massively pretrained language models make better storytellers"
    - "mask predict parallel decoding of conditional masked language models"
    - "mass masked sequence to sequence pre training for language generation"
    - "encoder agnostic adaptation for conditional language generation"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"

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)