human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adacare explainable clinical health status representation learning via scale adaptive feature extraction and recalibration"):
  Abstract: Deep learning-based health status representation learning and clinical prediction have raised much research interest in recent years. Existing models have shown superior performance, but there are still several major issues that have not been fully taken into consideration. First, the historical variation pattern of the biomarker in diverse time scales plays a vital role in indicating the health status, but it has not been explicitly extracted by existing works. Second, key factors that strongly indicate the health risk are different among patients. It is still challenging to adaptively make use of the features for patients in diverse conditions. Third, using prediction models as the black box will limit the reliability in clinical practice. However, none of the existing works can provide satisfying interpretability and meanwhile achieve high prediction performance. In this work, we develop a general health status representation learning model, named AdaCare. It can capture the long and short-term variations of biomarkers as clinical features to depict the health status in multiple time scales. It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability. We conduct a health risk prediction experiment on two real-world datasets. Experiment results indicate that AdaCare outperforms state-of-the-art approaches and provides effective interpretability, which is verifiable by clinical experts.

Includes members:
  Member "concare personalized clinical feature embedding via capturing the healthcare context":
    also connected through other hyperedges:
      (none)
  Member "identify susceptible locations in medical records via adversarial attacks on deep predictive models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "poba ga perturbation optimized black box adversarial attacks via genetic algorithm")
  Member "mime multilevel medical embedding of electronic health records for predictive healthcare":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepenroll patient trial matching with deep embedding and entailment prediction")
  Member "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "concare personalized clinical feature embedding via capturing the healthcare context")
  Member "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "retain interpretable predictive model in healthcare using reverse time attention mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bias in data driven ai systems an introductory survey")
  Member "multi scale context aggregation by dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention guided chained context aggregation for semantic segmentation")

Question: Please tell me which class the source paper 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)