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: "information theory of data privacy"
  Abstract: By combining Shannon's cryptography model with an assumption to the lower bound of adversaries' uncertainty to the queried dataset, we develop a secure Bayesian inference-based privacy model and then in some extent answer Dwork et al.'s question [1]: "why Bayesian risk factors are the right measure for privacy loss". #R##N#This model ensures an adversary can only obtain little information of each individual from the model's output if the adversary's uncertainty to the queried dataset is larger than the lower bound. Importantly, the assumption to the lower bound almost always holds, especially for big datasets. Furthermore, this model is flexible enough to balance privacy and utility: by using four parameters to characterize the assumption, there are many approaches to balance privacy and utility and to discuss the group privacy and the composition privacy properties of this model.

Connected through hyperedge HE-1 (induced by source paper "impact of prior knowledge and data correlation on privacy leakage a unified analysis"):
  with members:
    - "marginal release under local differential privacy"
    - "quantifying differential privacy under temporal correlations"
    - "strong and provably secure database access control"
    - "privacy preserving analysis of correlated data"
    - "on the differential privacy of bayesian inference"
    - "protecting locations with differential privacy under temporal correlations"
    - "blowfish privacy tuning privacy utility trade offs using policies"
    - "prolonging the hide and seek game optimal trajectory privacy for location based services"

Connected through hyperedge HE-2 (induced by source paper "on the information privacy model the group and composition privacy"):
  with members:
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "calibrating data to sensitivity in private data analysis"
    - "composition attacks and auxiliary information in data privacy"

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)