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: "ernie enhanced representation through knowledge integration"
  Abstract: We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the masking strategy of BERT, ERNIE is designed to learn language representation enhanced by knowledge masking strategies, which includes entity-level masking and phrase-level masking. Entity-level strategy masks entities which are usually composed of multiple words.Phrase-level strategy masks the whole phrase which is composed of several words standing together as a conceptual unit.Experimental results show that ERNIE outperforms other baseline methods, achieving new state-of-the-art results on five Chinese natural language processing tasks including natural language inference, semantic similarity, named entity recognition, sentiment analysis and question answering. We also demonstrate that ERNIE has more powerful knowledge inference capacity on a cloze test.

Connected through hyperedge HE-1 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "long and diverse text generation with planning based hierarchical variational model"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "do massively pretrained language models make better storytellers"
    - "mass masked sequence to sequence pre training for language generation"
    - "strategies for structuring story generation"

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"
    - "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"
    - "leveraging pre trained checkpoints for sequence generation tasks"

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"
    - "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"
    - "text summarization with pretrained encoders"

Connected through hyperedge HE-4 (induced by source paper "cbag conditional biomedical abstract generation"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "language models as knowledge bases"
    - "the deepfake detection challenge dfdc preview dataset"
    - "ctrl a conditional transformer language model for controllable generation"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "bert has a mouth and it must speak bert as a markov random field language model"

Connected through hyperedge HE-5 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "a neural model for generating natural language summaries of program subroutines"
    - "characterizing and detecting cuda program bugs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "aroma code recommendation via structural code search"
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "deep reinforcement learning for sequence to sequence models"
    - "active inductive logic programming for code search"

Connected through hyperedge HE-6 (induced by source paper "application of pre training models in named entity recognition"):
  with members:
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "publicly available clinical bert embeddings"
    - "roberta a robustly optimized bert pretraining approach"
    - "attention is all you need"
    - "transfer learning for named entity recognition with neural networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-7 (induced by source paper "overview of the ccks 2019 knowledge graph evaluation track entity relation event and qa"):
  with members:
    - "zero shot entity linking by reading entity descriptions"
    - "ipre a dataset for inter personal relationship extraction"
    - "deep contextualized word representations"
    - "neural architectures for named entity recognition"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "enteval a holistic evaluation benchmark for entity representations"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "joint learning of named entity recognition and entity linking"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluation benchmarks and learning criteriafor discourse aware sentence representations"
    - "zero shot entity linking by reading entity descriptions"
    - "socialiqa commonsense reasoning about social interactions"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "learning to denoise distantly labeled data for entity typing"

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)