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: "differentially private histograms for range sum queries a modular approach"
  Abstract: We focus on the problem of differentially private histogram publication, for range-sum query answering. Specifically, we derive a histogram from a given dataset, such that (i) it satisfies $\epsilon$-differential privacy, and (ii) it achieves high utility for queries that request the sum of contiguous histogram bins. Existing schemes are distinguished into two categories: fast but oblivious to utility optimizations that exploit the data characteristics, and data-aware but slow. We are the first to address this problem with emphasis on both efficiency and utility. Towards this goal, we formulate a principled approach, which defines a small set of simple modules, based on which we can devise a variety of more complex schemes. We first express the state-of-the-art methods in terms of these modules, which allows us to identify the performance bottlenecks. Next, we design novel efficient and effective schemes based on non-trivial module combinations. We experimentally evaluate all mechanisms on three real datasets with diverse characteristics, and demonstrate the benefits of our proposals over previous work.

Connected through hyperedge HE-1 (induced by source paper "ektelo a framework for defining differentially private computations"):
  with members:
    - "graphical model based estimation and inference for differential privacy"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "private selection from private candidates"
    - "synthesizing coupling proofs of differential privacy"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "lightdp towards automating differential privacy proofs"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "differentially private bayesian programming"

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)