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: "a predictive approach using deep feature learning for electronic medical records a comparative study"
  Abstract: Massive amount of electronic medical records accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to extract knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. In this paper, we propose a new predictive approach based on feature representation using deep feature learning and word embedding techniques. Our method uses different deep architectures for feature representation in higher-level abstraction to obtain effective and more robust features from EMRs, and then build prediction models on the top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled one is scarce. We investigate the performance of representation learning through a supervised approach. First, we apply our method on a small dataset related to a specific precision medicine problem, which focuses on prediction of left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk in a vulnerable demographic subgroup (African-Americans). Then we use two large datasets from eICU collaborative research database to predict the length of stay in Cardiac-ICU and Neuro-ICU based on high dimensional features. Finally we provide a comparative study and show that our predictive approach leads to better results in comparison with others.

Connected through hyperedge HE-1 (induced by source paper "a deep active survival analysis approach for precision treatment recommendations application of prostate cancer"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "deep learning for patient specific kidney graft survival analysis"
    - "machine learning for survival analysis a survey"
    - "active deep learning for classification of hyperspectral images"
    - "representation learning a review and new perspectives"

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)