human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "impact of prior knowledge and data correlation on privacy leakage a unified analysis"):
  Abstract: It has been widely understood that differential privacy can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named prior differential privacy (PDP), is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph and the multivariate Gaussian model, to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis.

Includes members:
  Member "information theory of data privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the information privacy model the group and composition privacy")
  Member "marginal release under local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "outis crypto assisted differential privacy on untrusted servers")
  Member "quantifying differential privacy under temporal correlations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "videodp a universal platform for video analytics with differential privacy")
  Member "strong and provably secure database access control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "securing databases from probabilistic inference")
  Member "privacy preserving analysis of correlated data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "on the differential privacy of bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting performance of asynchronous differentially private learning")
  Member "protecting locations with differential privacy under temporal correlations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "protect edge privacy in path publishing with differential privacy")
  Member "blowfish privacy tuning privacy utility trade offs using policies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privacy and utility preserving textual analysis via calibrated multivariate perturbations")

Question: Please tell me which class the source paper 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.LG(Machine Learning)