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: "doctor ai predicting clinical events via recurrent neural networks"
  Abstract: Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit. Doctor AI assesses the history of patients to make multilabel predictions (one label for each diagnosis or medication category). Based on separate blind test set evaluation, Doctor AI can perform differential diagnosis with up to 79% recall@30, significantly higher than several baselines. Moreover, we demonstrate great generalizability of Doctor AI by adapting the resulting models from one institution to another without losing substantial accuracy.

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 "zimm a deep learning model for long term and blurry relapses with non clinical claims data"):
  with members:
    - "scalpel3 a scalable open source library for healthcare claims databases"
    - "behrt transformer for electronic health records"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "clinical concept embeddings learned from massive sources of multimodal medical data"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "language models are an effective patient representation learning technique for electronic health record data"):
  with members:
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "leveraging clinical time series data for prediction a cautionary tale"
    - "universal language model fine tuning for text classification"
    - "scalable and accurate deep learning for electronic health records"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "improving palliative care with deep learning"

Connected through hyperedge HE-4 (induced by source paper "a deep learning approach to diagnosing multiple sclerosis from smartphone data"):
  with members:
    - "cxplain causal explanations for model interpretation under uncertainty"
    - "not to cry wolf distantly supervised multitask learning in critical care"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "attention is all you need"
    - "a unified approach to interpreting model predictions"
    - "multi task prediction of disease onsets from longitudinal lab tests"

Connected through hyperedge HE-5 (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-6 (induced by source paper "detecting and interpreting myocardial infarction using fully convolutional neural networks"):
  with members:
    - "deep neural network ensembles for time series classification"
    - "multivariate lstm fcns for time series classification"
    - "tsviz demystification of deep learning models for time series analysis"
    - "ecg heartbeat classification a deep transferable representation"
    - "lstm fully convolutional networks for time series classification"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (induced by source paper "time2vec learning a vector representation of time"):
  with members:
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "learning temporal point processes via reinforcement learning"
    - "neural decomposition of time series data for effective generalization"
    - "can recurrent neural networks warp time"
    - "deep reinforcement learning of marked temporal point processes"
    - "streaming graph neural networks"
    - "neural ordinary differential equations"

Connected through hyperedge HE-8 (induced by source paper "graph convolutional transformer learning the graphical structure of electronic health records"):
  with members:
    - "pre training of graph augmented transformers for medication recommendation"
    - "behrt transformer for electronic health records"
    - "inpatient2vec medical representation learning for inpatients"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "resset a recurrent model for sequence of sets with applications to electronic medical records"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "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.LG(Machine Learning)