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: "fadl federated autonomous deep learning for distributed electronic health record"
  Abstract: Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. We propose a new method, called Federated-Autonomous Deep Learning (FADL) that trains part of the model using all data sources in a distributed manner and other parts using data from specific data sources. We observed that FADL outperforms traditional federated learning strategy and conclude that balance between global and local training is an important factor to consider when design distributed machine learning methods , especially in healthcare.

Connected through hyperedge HE-1 (induced by source paper "federated uncertainty aware learning for distributed hospital ehr data"):
  with members:
    - "analyzing the role of model uncertainty for electronic health records"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "secure federated transfer learning"
    - "using kernel methods and model selection for prediction of preterm birth"
    - "communication efficient learning of deep networks from decentralized data"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "open set medical diagnosis"):
  with members:
    - "federated learning challenges methods and future directions"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "reducing network agnostophobia"
    - "scalable and accurate deep learning for electronic health records"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "towards open world recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "two stage federated phenotyping and patient representation learning"):
  with members:
    - "towards federated learning at scale system design"
    - "patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records"
    - "federated learning strategies for improving communication efficiency"
    - "loadaboost loss based adaboost federated machine learning on medical data"
    - "artificial neural networks condensation a strategy to facilitate adaption of machine learning in medical settings by reducing computational burden"
    - "learning patient representations from text"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-4 (induced by source paper "loadaboost loss based adaboost federated machine learning on medical data"):
  with members:
    - "patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records"
    - "two stage federated phenotyping and patient representation learning"
    - "federated learning strategies for improving communication efficiency"
    - "how to backdoor federated learning"
    - "artificial neural networks condensation a strategy to facilitate adaption of machine learning in medical settings by reducing computational burden"
    - "federated learning with non iid data"
    - "federated multi task learning"
    - "practical secure aggregation for federated learning on user held data"

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.CY(Computers and Society)