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: "joint privacy cost optimization in smart electricity metering systems"
  Abstract: Joint privacy-cost optimization is studied for a smart grid consumer, whose electricity consumption is monitored in almost real time by the utility provider (UP). It is assumed that an energy storage device, e.g., an electrical battery, is available to the consumer, which can be utilized both to achieve privacy and to reduce the energy cost by modifying the electricity consumption. Privacy is measured via the mean squared distance between the smart meter readings and a target load profile, while time-of-use pricing is considered to compute the electricity cost. The consumer also has the possibility to sell electricity back to the UP to further improve the privacy-cost trade-off. Two privacy-preserving energy management policies (EMPs) are proposed, which differ in the way the target load profile is characterized. Additionally, a simplified and more practical EMP, which optimizes the energy management less frequently, is considered. Numerical results are presented to compare the performances of these EMPs in terms of the privacy-cost trade-off they achieve, considering a number of privacy indicators.

Connected through hyperedge HE-1 (induced by source paper "universal mutual information privacy guarantees for smart meters"):
  with members:
    - "a tunable measure for information leakage"
    - "privacy aware smart metering progress and challenges"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "smart meter privacy via the trapdoor channel"
    - "smart meter privacy with renewable energy and a finite capacity battery"
    - "smart meter privacy for multiple users in the presence of an alternative energy source"
    - "smart meter privacy a utility privacy framework"
    - "mutual information and minimum mean square error in gaussian channels"

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.IT(Information Theory)