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: "enhancing clinical concept extraction with contextual embeddings"
  Abstract: Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (e.g., ELMo, BERT) have further pushed the state-of-the-art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText). Both off-the-shelf open-domain embeddings and pre-trained clinical embeddings from MIMIC-III are evaluated. We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings, and compare these on four concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015. We also analyze the impact of the pre-training time of a large language model like ELMo or BERT on the extraction performance. Last, we present an intuitive way to understand the semantic information encoded by contextual embeddings. Contextual embeddings pre-trained on a large clinical corpus achieves new state-of-the-art performances across all concept extraction tasks. The best-performing model outperforms all state-of-the-art methods with respective F1-measures of 90.25, 93.18 (partial), 80.74, and 81.65. We demonstrate the potential of contextual embeddings through the state-of-the-art performance these methods achieve on clinical concept extraction. Additionally, we demonstrate contextual embeddings encode valuable semantic information not accounted for in traditional word representations.

Connected through hyperedge HE-1 (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"
    - "publicly available clinical bert 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-2 (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"
    - "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"
    - "publicly available clinical bert embeddings"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "probing biomedical embeddings from language models"
    - "ml net multi label classification of biomedical texts with deep neural networks"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "transformer xl attentive language models beyond a fixed length context"
    - "publicly available clinical bert embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-4 (induced by source paper "uw bhi at mediqa 2019 an analysis of representation methods for medical natural language inference"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "publicly available clinical bert embeddings"
    - "syntactic scaffolds for semantic structures"
    - "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"
    - "semantic parsing for task oriented dialog using hierarchical representations"

Connected through hyperedge HE-5 (induced by source paper "extracting umls concepts from medical text using general and domain specific deep learning models"):
  with members:
    - "medmentions a large biomedical corpus annotated with umls concepts"
    - "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"
    - "publicly available clinical bert embeddings"
    - "joint entity extraction and assertion detection for clinical text"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "clinical concept extraction with contextual word embedding"

Connected through hyperedge HE-6 (induced by source paper "end to end named entity recognition and relation extraction using pre trained language models"):
  with members:
    - "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"
    - "entity relation extraction as multi turn question answering"
    - "end to end neural relation extraction using deep biaffine attention"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "publicly available clinical bert embeddings"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "a deep learning architecture for de identification of patient notes implementation and evaluation"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "clinical concept extraction with contextual word embedding"

Connected through hyperedge HE-8 (induced by source paper "knowledge guided named entity recognition"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "scibert a pretrained language model for scientific text"
    - "question answering is a format when is it useful"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "exploring ways to incorporate additional knowledge to improve natural language commonsense question answering"
    - "careful selection of knowledge to solve open book question answering"
    - "the natural language decathlon multitask learning as question answering"

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)