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 "differentially private data releasing for smooth queries with synthetic database output"):
  Abstract: We consider accurately answering smooth queries while preserving differential privacy. A query is said to be $K$-smooth if it is specified by a function defined on $[-1,1]^d$ whose partial derivatives up to order $K$ are all bounded. We develop an $\epsilon$-differentially private mechanism for the class of $K$-smooth queries. The major advantage of the algorithm is that it outputs a synthetic database. In real applications, a synthetic database output is appealing. Our mechanism achieves an accuracy of $O (n^{-\frac{K}{2d+K}}/\epsilon )$, and runs in polynomial time. We also generalize the mechanism to preserve $(\epsilon, \delta)$-differential privacy with slightly improved accuracy. Extensive experiments on benchmark datasets demonstrate that the mechanisms have good accuracy and are efficient.

Includes members:
  Member "the noisy power method a meta algorithm with applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "efficient algorithms for privately releasing marginals via convex relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the power of factorization mechanisms in local and central differential privacy")
  Member "faster algorithms for privately releasing marginals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data dependent differentially private parameter learning for directed graphical models")
  Member "differentially private online learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating differentially private machine learning in practice")
  Member "a learning theory approach to non interactive database privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "submodular functions are noise stable":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "private data release via learning thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the power of factorization mechanisms in local and central differential privacy")
  Member "a simple and practical algorithm for differentially private data release":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a workload adaptive mechanism for linear queries under local differential 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.DB(Databases)