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: "optimizing histogram queries under differential privacy"
  Abstract: Differential privacy is a robust privacy standard that has been successfully applied to a range of data analysis tasks. Despite much recent work, optimal strategies for answering a collection of correlated queries are not known. #R##N#We study the problem of devising a set of strategy queries, to be submitted and answered privately, that will support the answers to a given workload of queries. We propose a general framework in which query strategies are formed from linear combinations of counting queries, and we describe an optimal method for deriving new query answers from the answers to the strategy queries. Using this framework we characterize the error of strategies geometrically, and we propose solutions to the problem of finding optimal strategies.

Connected through hyperedge HE-1 (induced by source paper "design of policy aware differentially private algorithms"):
  with members:
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "blowfish privacy tuning privacy utility trade offs using policies"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"
    - "optimal error of query sets under the differentially private matrix mechanism"
    - "fairness through awareness"
    - "personalized social recommendations accurate or private"

Connected through hyperedge HE-2 (induced by source paper "blowfish privacy tuning privacy utility trade offs using policies"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "optimal error of query sets under the differentially private matrix mechanism"
    - "personalized social recommendations accurate or private"
    - "differential privacy via wavelet transforms"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-3 (induced by source paper "boosting the accuracy of differentially private histograms through consistency"):
  with members:
    - "a learning theory approach to non interactive database privacy"
    - "differential privacy via wavelet transforms"
    - "on the geometry of differential privacy"
    - "interactive privacy via the median mechanism"
    - "universally utility maximizing privacy mechanisms"

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.DB(Databases)