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 "optimal error of query sets under the differentially private matrix mechanism"):
  Abstract: A common goal of privacy research is to release synthetic data that satisfies a formal privacy guarantee and can be used by an analyst in place of the original data. To achieve reasonable accuracy, a synthetic data set must be tuned to support a specified set of queries accurately, sacrificing fidelity for other queries. #R##N#This work considers methods for producing synthetic data under differential privacy and investigates what makes a set of queries "easy" or "hard" to answer. We consider answering sets of linear counting queries using the matrix mechanism, a recent differentially-private mechanism that can reduce error by adding complex correlated noise adapted to a specified workload. #R##N#Our main result is a novel lower bound on the minimum total error required to simultaneously release answers to a set of workload queries. The bound reveals that the hardness of a query workload is related to the spectral properties of the workload when it is represented in matrix form. The bound is most informative for $(\epsilon,\delta)$-differential privacy but also applies to $\epsilon$-differential privacy.

Includes members:
  Member "the geometry of differential privacy the sparse and approximate cases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "an adaptive mechanism for accurate query answering under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "accurate and efficient private release of datacubes and contingency tables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privtree a differentially private algorithm for hierarchical decompositions")
  Member "low rank mechanism for optimizing batch queries under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convex optimization for linear query processing under approximate differential privacy")
  Member "a learning theory approach to non interactive database privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "differentially private spatial decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "outis crypto assisted differential privacy on untrusted servers")
  Member "compressive mechanism utilizing sparse representation in differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")

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.DB(Databases)