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: "local information privacy and its application to privacy preserving data aggregation"
  Abstract: In this paper, we study local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users' privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP, which can be viewed as explicit modeling of the adversary's background knowledge. It enables the design of privacy-preserving mechanisms leveraging the prior distribution, which can potentially achieve a better utility-privacy tradeoff than context-free notions such as Local Differential Privacy (LDP). We present an optimization framework to minimize the mean square error in the data aggregation while protecting the privacy of each individual user's input data or a correlated latent variable while satisfying LIP constraints. Then, we study two different types of applications: (weighted) summation and histogram estimation, and derive the optimal context-aware data perturbation parameters for each case, based on randomized response type of mechanism. We further compare the utility-privacy tradeoff between LIP and LDP and theoretically explain why the incorporation of prior knowledge enlarges feasible regions of the perturbation parameters, which thereby leads to higher utility. We also extend the LIP-based privacy mechanisms to the more general case when exact prior knowledge is not available. Finally, we validate our analysis by simulations using both synthetic and real-world data. Results show that our LIP-based privacy mechanism provides better utility-privacy tradeoffs than LDP, and the advantage of LIP is even more significant when the prior distribution is more skewed.

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.CR(Cryptography and Security)