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 "privtree a differentially private algorithm for hierarchical decompositions"):
  Abstract: Given a set D of tuples defined on a domain Omega, we study differentially private algorithms for constructing a histogram over Omega to approximate the tuple distribution in D. Existing solutions for the problem mostly adopt a hierarchical decomposition approach, which recursively splits Omega into sub-domains and computes a noisy tuple count for each sub-domain, until all noisy counts are below a certain threshold. This approach, however, requires that we (i) impose a limit h on the recursion depth in the splitting of Omega and (ii) set the noise in each count to be proportional to h. This leads to inferior data utility due to the following dilemma: if we use a small h, then the resulting histogram would be too coarse-grained to provide an accurate approximation of data distribution; meanwhile, a large h would yield a fine-grained histogram, but its quality would be severely degraded by the increased amount of noise in the tuple counts. #R##N#To remedy the deficiency of existing solutions, we present PrivTree, a histogram construction algorithm that also applies hierarchical decomposition but features a crucial (and somewhat surprising) improvement: when deciding whether or not to split a sub-domain, the amount of noise required in the corresponding tuple count is independent of the recursive depth. This enables PrivTree to adaptively generate high-quality histograms without even asking for a pre-defined threshold on the depth of sub-domain splitting. As concrete examples, we demonstrate an application of PrivTree in modelling spatial data, and show that it can also be extended to handle sequence data (where the decision in sub-domain splitting is not based on tuple counts but a more sophisticated measure). Our experiments on a variety of real datasets show that PrivTree significantly outperforms the states of the art in terms of data utility.

Includes members:
  Member "on the privacy properties of variants on the sparse vector technique":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "minimax rates of estimating approximate differential privacy")
  Member "differentially private k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentially private k means clustering with guaranteed convergence")
  Member "a data and workload aware algorithm for range queries under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentially private m band wavelet based mechanisms in machine learning environments")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locally private hypothesis selection")
  Member "privbasis frequent itemset mining with differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evaluating differentially private machine learning in practice")
  Member "an adaptive mechanism for accurate query answering under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "dual query practical private query release for high dimensional data")
  Member "optimal error of query sets under the differentially private matrix mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "linear and range counting under metric based 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)