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 review of privacy and consent management in healthcare a focus on emerging data sources"
  Abstract: The emergence of New Data Sources (NDS) in healthcare is revolutionising traditional electronic health records in terms of data availability, storage, and access. Increasingly, clinicians are using NDS to build a virtual holistic image of a patient's health condition. This research is focused on a review and analysis of the current legislation and privacy rules available for healthcare professionals. NDS in this project refers to and includes patient-generated health data, consumer device data, wearable health and fitness data, and data from social media. #R##N#This project reviewed legal and regulatory requirements for New Zealand, Australia, the European Union, and the United States to establish the ground reality of existing mechanisms in place concerning the use of NDS. The outcome of our research is to recommend changes and enhancements required to better prepare for the 'tsunami' of NDS and applications in the currently evolving data-driven healthcare area and precision or personalised health initiatives such as Precision Driven Health (PDH) in New Zealand.

Connected through hyperedge HE-1 (induced by source paper "characterizing iot data and its quality for use"):
  with members:
    - "towards a data driven iot software architecture for smart city utilities"
    - "a trust model for data sharing in smart cities"
    - "holistic measures for evaluating prediction models in smart grids"
    - "when things matter a data centric view of the internet of things"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving action recognition for smart hospitals using low resolution depth images"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
    - "low resolution face recognition in the wild"
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "low resolution face recognition using a two branch deep convolutional neural network architecture"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-3 (induced by source paper "a fully private pipeline for deep learning on electronic health records"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "deep learning for metagenomic data using 2d embeddings and convolutional neural networks"
    - "identifying diabetic patients with high risk of readmission"
    - "globally optimal training of generalized polynomial neural networks with nonlinear spectral methods"
    - "faster arithmetic for number theoretic transforms"

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)