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: "learning hierarchical representations of electronic health records for clinical outcome prediction"
  Abstract: Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the longand irregular clinical event sequences. We make the observation that clinical events at a long time scale exhibit strongtemporal patterns, while events within a short time period tend to be disordered co-occurrence. We thus propose differentiated mechanisms to model clinical events at different time scales. Our model learns hierarchical representationsof event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture coretemporal dependencies. Experimental results on real clinical data show that our model greatly improves over previous state-of-the-art models, achieving AUC scores of 0.94 and 0.90 for predicting death and ICU admission respectively, Our model also successfully identifies important events for different clinical outcome prediction tasks

Connected through hyperedge HE-1 (induced by source paper "adaptive prediction timing for electronic health records"):
  with members:
    - "analyzing the role of model uncertainty for electronic health records"
    - "dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre processing"
    - "interpolation prediction networks for irregularly sampled time series"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "scalable and accurate deep learning for electronic health records"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "adaptive computation time for recurrent neural networks"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-2 (induced by source paper "concare personalized clinical feature embedding via capturing the healthcare context"):
  with members:
    - "r transformer recurrent neural network enhanced transformer"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "attention is all you need"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "gram graph based attention model for healthcare representation learning"
    - "wavenet a generative model for raw audio"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-3 (induced by source paper "predictive multi level patient representations from electronic health records"):
  with members:
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "attention is all you need"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-4 (induced by source paper "early prediction of sepsis from clinical datavia heterogeneous event aggregation"):
  with members:
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "attention is all you need"

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)