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: "privacy in information rich intelligent infrastructure"
  Abstract: Intelligent infrastructure will critically rely on the dense instrumentation of sensors and actuators that constantly transmit streaming data to cloud-based analytics for real-time monitoring. For example, driverless cars communicate real-time location and other data to companies like Google, which aggregate regional data in order to provide real-time traffic maps. Such traffic maps can be extremely useful to the driver (for optimal travel routing), as well as to city transportation administrators for real-time accident response that can have an impact on traffic capacity. Intelligent infrastructure monitoring compromises the privacy of drivers who continuously share their location to cloud aggregators, with unpredictable consequences. #R##N#Without a framework for protecting the privacy of the driver's data, drivers may be very conservative about sharing their data with cloud-based analytics that will be responsible for adding the intelligence to intelligent infrastructure. In the energy sector, the Smart Grid revolution relies critically on real-time metering of energy supply and demand with very high granularity. This is turn enables real-time demand response and creates a new energy market that can incorporate unpredictable renewable energy sources while ensuring grid stability and reliability. However, real-time streaming data captured by smart meters contain a lot of private information, such as our home activities or lack of, which can be easily inferred by anyone that has access to the smart meter data, resulting not only in loss of privacy but potentially also putting us at risk.

Connected through hyperedge HE-1 (induced by source paper "differentially private draw and discard machine learning"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "revisiting small batch training for deep neural networks"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "renyi differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "concentrated differential privacy simplifications extensions and lower bounds"

Connected through hyperedge HE-2 (induced by source paper "private continual release of real valued data streams"):
  with members:
    - "psi ψ a private data sharing interface"
    - "private decayed predicate sums on streams"

Connected through hyperedge HE-3 (induced by source paper "privacy loss in apple s implementation of differential privacy on macos 10 12"):
  with members:
    - "proving differential privacy via probabilistic couplings"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "proving differential privacy in hoare logic"
    - "differential privacy an economic method for choosing epsilon"
    - "calibrating data to sensitivity in private data analysis"

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)