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: "clinicalbert modeling clinical notes and predicting hospital readmission"
  Abstract: Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structured data, because notes are high-dimensional and sparse. This work develops and evaluates representations of clinical notes using bidirectional transformers (ClinicalBERT). ClinicalBERT uncovers high-quality relationships between medical concepts as judged by humans. ClinicalBert outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intensive care unit. Code and model parameters are available.

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"
    - "not enough data deep learning to the rescue"
    - "huggingface s transformers state of the art natural language processing"
    - "publicly available clinical bert embeddings"

Connected through hyperedge HE-2 (induced by source paper "application of pre training models in named entity recognition"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "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-3 (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"
    - "publicly available clinical bert embeddings"
    - "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-4 (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"
    - "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-5 (induced by source paper "scibert a pretrained language model for scientific text"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "from pos tagging to dependency parsing for biomedical event extraction"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "publicly available clinical bert embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"

Connected through hyperedge HE-6 (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"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "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-7 (induced by source paper "taper time aware patient ehr representation"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "dynamic feature acquisition using denoising autoencoders"
    - "publicly available clinical bert embeddings"
    - "explainable prediction of medical codes from clinical text"
    - "medical concept embedding with time aware attention"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "deep ehr chronic disease prediction using medical notes"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (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"
    - "publicly available clinical bert embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "clinical concept extraction with contextual word embedding"
    - "recurrent neural networks with specialized word embeddings for health domain named entity recognition"
    - "attention is all you need"
    - "bidirectional attention flow for machine comprehension"

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)