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: "clinical concept extraction with contextual word embedding"
  Abstract: Automatic extraction of clinical concepts is an essential step for turning the unstructured data within a clinical note into structured and actionable information. In this work, we propose a clinical concept extraction model for automatic annotation of clinical problems, treatments, and tests in clinical notes utilizing domain-specific contextual word embedding. A contextual word embedding model is first trained on a corpus with a mixture of clinical reports and relevant Wikipedia pages in the clinical domain. Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model. We tested our proposed model on the I2B2 2010 challenge dataset. Our proposed model achieved the best performance among reported baseline models and outperformed the state-of-the-art models by 3.4% in terms of F1-score.

Connected through hyperedge HE-1 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing"):
  with members:
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "explainable prediction of medical codes from clinical text"
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "medical concept embedding with time aware attention"
    - "patient representation learning and interpretable evaluation using clinical notes"
    - "semantic structure and interpretability of word embeddings"
    - "improving medical short text classification with semantic expansion using word cluster embedding"

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"
    - "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"
    - "publicly available clinical bert embeddings"

Connected through hyperedge HE-3 (induced by source paper "probing biomedical embeddings from language models"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "deep contextualized word representations"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"
    - "dissecting contextual word embeddings architecture and representation"

Connected through hyperedge HE-4 (induced by source paper "biobert a pre trained biomedical language representation model for biomedical text mining"):
  with members:
    - "medmentions a large biomedical corpus annotated with umls concepts"
    - "a silver standard corpus of human phenotype gene relations"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "publicly available clinical bert embeddings"
    - "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-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"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "pre trained language model for biomedical question answering"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "using neural networks for relation extraction from biomedical literature"
    - "publicly available clinical bert embeddings"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-7 (induced by source paper "enhancing clinical concept extraction with contextual embeddings"):
  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"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "recurrent neural networks with specialized word embeddings for health domain named entity recognition"
    - "attention is all you need"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-8 (induced by source paper "publicly available clinical bert embeddings"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "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"

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)