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: "raim recurrent attentive and intensive model of multimodal patient monitoring data"
  Abstract: With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). However, it becomes increasingly challenging to model such data, due to high density of the monitoring data, heterogeneous data types and the requirement for interpretable models. Integration of these high-density monitoring data with the discrete clinical events (including diagnosis, medications, labs) is challenging but potentially rewarding since richness and granularity in such multimodal data increase the possibilities for accurate detection of complex problems and predicting outcomes (e.g., length of stay and mortality). We propose Recurrent Attentive and Intensive Model (RAIM) for jointly analyzing continuous monitoring data and discrete clinical events. RAIM introduces an efficient attention mechanism for continuous monitoring data (e.g., ECG), which is guided by discrete clinical events (e.g, medication usage). We apply RAIM in predicting physiological decompensation and length of stay in those critically ill patients at ICU. With evaluations on MIMIC- III Waveform Database Matched Subset, we obtain an AUC-ROC score of 90.18% for predicting decompensation and an accuracy of 86.82% for forecasting length of stay with our final model, which outperforms our six baseline models.

Connected through hyperedge HE-1 (induced by source paper "what went wrong and when instance wise feature importance for time series models"):
  with members:
    - "is attention interpretable"
    - "what clinicians want contextualizing explainable machine learning for clinical end use"
    - "attention is not explanation"
    - "tsviz demystification of deep learning models for time series analysis"
    - "explaining an increase in predicted risk for clinical alerts"
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-2 (induced by source paper "exploring interpretable lstm neural networks over multi variable data"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "deep multi output forecasting learning to accurately predict blood glucose trajectories"
    - "methods for interpreting and understanding deep neural networks"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "a dual stage attention based recurrent neural network for time series prediction"

Connected through hyperedge HE-3 (induced by source paper "what clinicians want contextualizing explainable machine learning for clinical end use"):
  with members:
    - "model cards for model reporting"
    - "attention is not explanation"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "opportunities in machine learning for healthcare"
    - "clinicalvis supporting clinical task focused design evaluation"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "manipulating and measuring model interpretability"

Connected through hyperedge HE-4 (induced by source paper "time guided high order attention model of longitudinal heterogeneous healthcare data"):
  with members:
    - "medical concept embedding with time aware attention"
    - "the use of autoencoders for discovering patient phenotypes"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "learning robust features using deep learning for automatic seizure detection"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "analyzing the role of model uncertainty for electronic health records"):
  with members:
    - "measuring calibration in deep learning"
    - "bayesian layers a module for neural network uncertainty"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "scalable and accurate deep learning for electronic health records"
    - "on calibration of modern neural networks"
    - "bayesian recurrent neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "using clinical notes with time series data for icu management"):
  with members:
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "explainable prediction of medical codes from clinical text"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "rationale augmented convolutional neural networks for text classification"
    - "explain images with multimodal recurrent neural networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "machine learning for early prediction of circulatory failure in the intensive care unit"):
  with members:
    - "deepsofa a continuous acuity score for critically ill patients using clinically interpretable deep learning"
    - "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"

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)