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: "deepsofa a continuous acuity score for critically ill patients using clinically interpretable deep learning"
  Abstract: Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time-sensitive individual physiological patterns, a critical task in the intensive care unit. We propose a novel acuity score framework (DeepSOFA) that leverages temporal measurements and interpretable deep learning models to assess illness severity at any point during an ICU stay. We compare DeepSOFA with SOFA (Sequential Organ Failure Assessment) baseline models using the same model inputs and find that at any point during an ICU admission, DeepSOFA yields significantly more accurate predictions of in-hospital mortality. A DeepSOFA model developed in a public database and validated in a single institutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI 0.90–0.91) compared with baseline SOFA models with mean AUC 0.79 (95% CI 0.79–0.80) and 0.85 (95% CI 0.85–0.86). Deep models are well-suited to identify ICU patients in need of life-saving interventions prior to the occurrence of an unexpected adverse event and inform shared decision-making processes among patients, providers, and families regarding goals of care and optimal resource utilization.

Connected through hyperedge HE-1 (induced by source paper "a physiology driven computational model for post cardiac arrest outcome prediction"):
  with members:
    - "hctsa a computational framework for automated time series phenotyping using massive feature extraction"
    - "highly comparative feature based time series classification"

Connected through hyperedge HE-2 (induced by source paper "explainable artificial intelligence model to predict acute critical illness from electronic health records"):
  with members:
    - "stcn stochastic temporal convolutional networks"
    - "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping"
    - "early detection of sepsis utilizing deep learning on electronic health record event sequences"
    - "explainable ai for trees from local explanations to global understanding"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "machine learning for early prediction of circulatory failure in the intensive care unit"):
  with members:
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "scalable and accurate deep learning for electronic health records"
    - "a unified approach to interpreting model predictions"
    - "improving palliative care with deep learning"
    - "predicting severe sepsis using text from the electronic health record"
    - "multi task prediction of disease onsets from longitudinal lab tests"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-4 (induced by source paper "hybrid text feature modeling for disease group prediction using unstructured physician notes"):
  with members:
    - "efficient estimation of word representations in vector space"

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)