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: "publicly available clinical bert embeddings"
  Abstract: Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on three common clinical NLP tasks as compared to nonspecific embeddings. These domain-specific models are not as performant on two clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text.

Connected through hyperedge HE-1 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records"):
  with members:
    - "secnlp a survey of embeddings in clinical natural language processing"
    - "bpe dropout simple and effective subword regularization"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "named entity recognition in electronic health records using transfer learning bootstrapped neural networks"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "not enough data deep learning to the rescue"
    - "huggingface s transformers state of the art natural language processing"

Connected through hyperedge HE-2 (induced by source paper "deepenroll patient trial matching with deep embedding and entailment prediction"):
  with members:
    - "pre training of graph augmented transformers for medication recommendation"
    - "learning eligibility in cancer clinical trials using deep neural networks"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "identity aware textual visual matching with latent co attention"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "federated pretraining and fine tuning of bert using clinical notes from multiple silos"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "what does bert look at an analysis of bert s attention"
    - "two stage federated phenotyping and patient representation learning"
    - "federated learning strategies for improving communication efficiency"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "application of pre training models in named entity recognition"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "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-5 (induced by source paper "mt bioner multi task learning for biomedical named entity recognition using deep bidirectional transformers"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "multi task deep neural networks for natural language understanding"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "audio de identification a new entity recognition task"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "automated labelling using an attention model for radiology reports of mri scans alarm"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "classification of radiology reports using neural attention models"
    - "attention is all you need"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "uncc biomedical semantic question answering systems bioasq task 7b phase b"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "allennlp a deep semantic natural language processing platform"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural question answering at bioasq 5b"
    - "squad 100 000 questions for machine comprehension of text"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "representation learning for electronic health records"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "multimodal multitask representation learning for pathology biobank metadata prediction"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "clinical concept extraction for document level coding"
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "unsupervised clinical language translation"
    - "xlnet generalized autoregressive pretraining for language understanding"

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)