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: "safs a deep feature selection approach for precision medicine"
  Abstract: In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prioritizing risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach is to use deep learning to identify significant risk factors affecting left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk. The results show that our feature learning and representation approach leads to better results in comparison with others.

Connected through hyperedge HE-1 (induced by source paper "representation learning with autoencoders for electronic health records a comparative study"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "a survey on deep learning in medical image analysis"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "adversarial images for variational autoencoders"
    - "deepqa improving the estimation of single protein model quality with deep belief networks"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "a predictive approach using deep feature learning for electronic medical records a comparative study"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "subic a supervised bi clustering approach for precision medicine"
    - "multi task prediction of disease onsets from longitudinal lab tests"
    - "adversarial images for variational autoencoders"
    - "deepqa improving the estimation of single protein model quality with deep belief networks"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "dropout feature ranking for deep learning models"):
  with members:
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "clinical intervention prediction and understanding using deep networks"
    - "learning important features through propagating activation differences"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "why should i trust you explaining the predictions of any classifier"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-4 (induced by source paper "subic a supervised bi clustering approach for precision medicine"):
  with members:
    - "objective criteria for the evaluation of clustering methods"

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)