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: "deep ehr chronic disease prediction using medical notes"
  Abstract: Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of different deep learning architectures including CNN, LSTM and hierarchical models.In contrast to traditional text-based prediction models, our approach does not require disease specificfeature engineering, and can handle negations and numerical values that exist in the text. Ourresults on a cohort of about 1 million patients show that models using text outperform modelsusing just structured data, and that models capable of using numerical values and negations in thetext, in addition to the raw text, further improve performance. Additionally, we compare differentvisualization methods for medical professionals to interpret model predictions.

Connected through hyperedge HE-1 (induced by source paper "improved hierarchical patient classification with language model pretraining over clinical notes"):
  with members:
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scalable and accurate deep learning for electronic health records"
    - "hierarchical rnn with static sentence level attention for text based speaker change detection"

Connected through hyperedge HE-2 (induced by source paper "towards automatic generation of shareable synthetic clinical notes using neural language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards the creation of a large corpus of synthetically identified clinical notes"
    - "fine tuned language models for text classification"
    - "lessons from natural language inference in the clinical domain"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "towards measuring membership privacy"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "de identification of patient notes with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "taper time aware patient ehr representation"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "clinicalbert modeling clinical notes and predicting hospital readmission"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "publicly available clinical bert embeddings"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scalable and accurate deep learning for electronic health records"
    - "attention is all you need"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "benchmarking deep learning architectures for predicting readmission to the icu and describing patients at risk"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "medical concept embedding with time aware attention"
    - "resset a recurrent model for sequence of sets with applications to electronic medical records"
    - "scalable and accurate deep learning for electronic health records"
    - "discrete event continuous time rnns"
    - "recurrent neural networks for multivariate time series with missing values"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "explainable prediction of adverse outcomes using clinical notes"):
  with members:
    - "attention is not not explanation"
    - "an analysis of attention over clinical notes for predictive tasks"
    - "is attention interpretable"
    - "attention is not explanation"
    - "explainable prediction of medical codes from clinical text"
    - "multimodal machine learning for automated icd coding"
    - "starspace embed all the things"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "savehr self attention vector representations for ehr based personalized chronic disease onset prediction and interpretability"):
  with members:
    - "effectiveness of lstms in predicting congestive heart failure onset"
    - "a structured self attentive sentence embedding"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-8 (induced by source paper "improving hospital mortality prediction with medical named entities and multimodal learning"):
  with members:
    - "clinical intervention prediction and understanding using deep networks"
    - "deep active learning for named entity recognition"
    - "efficient vector representation for documents through corruption"
    - "multi task prediction of disease onsets from longitudinal lab tests"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "recurrent neural networks for multivariate time series with missing values"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"

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.LG(Machine Learning)