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 the joint representation of heterogeneous temporal events for clinical endpoint prediction"
  Abstract: The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records. This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications. However, the problem is very challenging since patients' history records contain multiple heterogeneous temporal events such as lab tests, diagnosis, and drug administrations. The visiting patterns of different types of events vary significantly, and there exist complex nonlinear relationships between different events. In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of our proposed approach over competitive baselines.

Connected through hyperedge HE-1 (induced by source paper "concare personalized clinical feature embedding via capturing the healthcare context"):
  with members:
    - "learning hierarchical representations of electronic health records for clinical outcome prediction"
    - "r transformer recurrent neural network enhanced transformer"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "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-2 (induced by source paper "inpatient2vec medical representation learning for inpatients"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "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"
    - "attention is all you need"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "gram graph based attention model for healthcare representation learning"

Connected through hyperedge HE-3 (induced by source paper "edge aibench towards comprehensive end to end edge computing benchmarking"):
  with members:
    - "aibench an industry standard internet service ai benchmark suite"
    - "federated machine learning concept and applications"
    - "ai benchmark running deep neural networks on android smartphones"
    - "edgebench benchmarking edge computing platforms"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "facenet a unified embedding for face recognition and clustering"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-4 (induced by source paper "adacare explainable clinical health status representation learning via scale adaptive feature extraction and recalibration"):
  with members:
    - "concare personalized clinical feature embedding via capturing the healthcare context"
    - "identify susceptible locations in medical records via adversarial attacks on deep predictive models"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "wavenet a generative model for raw audio"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "learning hierarchical representations of electronic health records for clinical outcome prediction"):
  with members:
    - "towards automated icd coding using deep learning"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "recurrent neural networks for multivariate time series with missing values"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "predictive multi level patient representations from electronic health records"):
  with members:
    - "learning hierarchical representations of electronic health records for clinical outcome prediction"
    - "attention is all you need"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-7 (induced by source paper "early prediction of sepsis from clinical datavia heterogeneous event aggregation"):
  with members:
    - "learning hierarchical representations of electronic health records for clinical outcome 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.AI(Artificial Intelligence)