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 "differential privacy with partial knowledge"):
  Abstract: Differential privacy offers formal quantitative guarantees for algorithms over datasets, but it assumes attackers that know and can influence all but one record in the database. This assumption often vastly overapproximates the attackers' actual strength, resulting in unnecessarily poor utility.  Recent work has made significant steps towards privacy in the presence of partial background knowledge, which can model a realistic attacker's uncertainty. Prior work, however, has definitional problems for correlated data and does not precisely characterize the underlying attacker model. We propose a practical criterion to prevent problems due to correlations, and we show how to characterize attackers with limited influence or only partial background knowledge over the dataset. We use these foundations to analyze practical scenarios: we significantly improve known results about the privacy of counting queries under partial knowledge, and we show that thresholding can provide formal guarantees against such weak attackers, even with little entropy in the data. Finally, we prove composition results on differential privacy with partial knowledge, which quantifies the privacy leakage of complex mechanisms.  Together, our results provide a basis for formally quantifying the privacy of many widely-used mechanisms, e.g. publishing the result of surveys, elections or referendums, and releasing usage statistics of online services.

Includes members:
  Member "the privacy blanket of the shuffle model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "renyi differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "differential privacy as a causal property":
    also connected through other hyperedges:
      (none)
  Member "towards extending noiseless privacy dependent data and more practical approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cardinality estimators do not preserve privacy")

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.CR(Cryptography and Security)