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: "going digital a survey on digitalization and large scale data analytics in healthcare"
  Abstract: We provide an overview of the recent trends towards digitalization and large scale data analytics in healthcare. It is expected that these trends are instrumental in the dramatic changes in the way healthcare will be organized in the future. We discuss the recent political initiatives designed to shift care delivery processes from paper to electronic, with the goals of more effective treatments with better outcomes; cost pressure is a major driver of innovation. We describe newly developed networks of healthcare providers, research organizations and commercial vendors to jointly analyze data for the development of decision support systems. We address the trend towards continuous healthcare where health is monitored by wearable and stationary devices; a related development is that patients increasingly assume responsibility for their own health data. Finally we discuss recent initiatives towards a personalized medicine, based on advances in molecular medicine, data management, and data analytics.

Connected through hyperedge HE-1 (induced by source paper "model aggregation via good enough model spaces"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "multi institutional deep learning modeling without sharing patient data a feasibility study on brain tumor segmentation"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "overcoming catastrophic forgetting in neural networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "communication complexity of distributed convex learning and optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "multi institutional deep learning modeling without sharing patient data a feasibility study on brain tumor segmentation"):
  with members:
    - "how to backdoor federated learning"
    - "federated learning with non iid data"
    - "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
    - "differentially private federated learning a client level perspective"
    - "learning differentially private recurrent language models"
    - "overcoming catastrophic forgetting in neural networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "predictive clinical decision support system with rnn encoding and tensor decoding"):
  with members:
    - "predicting clinical events by combining static and dynamic information using recurrent neural networks"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "theano new features and speed improvements"

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)