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: "on the privacy properties of variants on the sparse vector technique"
  Abstract: The sparse vector technique is a powerful differentially private primitive that allows an analyst to check whether queries in a stream are greater or lesser than a threshold. This technique has a unique property -- the algorithm works by adding noise with a finite variance to the queries and the threshold, and guarantees privacy that only degrades with (a) the maximum sensitivity of any one query in stream, and (b) the number of positive answers output by the algorithm. Recent work has developed variants of this algorithm, which we call {\em generalized private threshold testing}, and are claimed to have privacy guarantees that do not depend on the number of positive or negative answers output by the algorithm. These algorithms result in a significant improvement in utility over the sparse vector technique for a given privacy budget, and have found applications in frequent itemset mining, feature selection in machine learning and generating synthetic data. #R##N#In this paper we critically analyze the privacy properties of generalized private threshold testing. We show that generalized private threshold testing does not satisfy \epsilon-differential privacy for any finite \epsilon. We identify a subtle error in the privacy analysis of this technique in prior work. Moreover, we show an adversary can use generalized private threshold testing to recover counts from the datasets (especially small counts) exactly with high accuracy, and thus can result in individuals being reidentified. We demonstrate our attacks empirically on real datasets.

Connected through hyperedge HE-1 (induced by source paper "minimax rates of estimating approximate differential privacy"):
  with members:
    - "proving differential privacy with shadow execution"
    - "generative adversarial privacy"
    - "property testing for differential privacy"
    - "detecting violations of differential privacy"
    - "pacgan the power of two samples in generative adversarial networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"

Connected through hyperedge HE-2 (induced by source paper "fuzzi a three level logic for differential privacy"):
  with members:
    - "approximate span liftings compositional semantics for relaxations of differential privacy"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "synthesizing coupling proofs of differential privacy"
    - "learning differentially private recurrent language models"
    - "towards practical differential privacy for sql queries"
    - "lightdp towards automating differential privacy proofs"
    - "probabilistic couplings for probabilistic reasoning"

Connected through hyperedge HE-3 (induced by source paper "detecting violations of differential privacy"):
  with members:
    - "synthesizing coupling proofs of differential privacy"
    - "relational symbolic execution"
    - "renyi differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"

Connected through hyperedge HE-4 (induced by source paper "lightdp towards automating differential privacy proofs"):
  with members:
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "advanced probabilistic couplings for differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "proving differential privacy via probabilistic couplings"
    - "differentially private bayesian programming"
    - "higher order approximate relational refinement types for mechanism design and differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "proving differential privacy in hoare logic"

Connected through hyperedge HE-5 (induced by source paper "privtree a differentially private algorithm for hierarchical decompositions"):
  with members:
    - "differentially private k means clustering"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "privbasis frequent itemset mining with differential privacy"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "accurate and efficient private release of datacubes and contingency tables"
    - "optimal error of query sets under the differentially private matrix mechanism"
    - "differentially private grids for geospatial data"

Connected through hyperedge HE-6 (induced by source paper "proving differential privacy via probabilistic couplings"):
  with members:
    - "computer aided verification for mechanism design"
    - "understanding the sparse vector technique for differential privacy"
    - "relational reasoning via probabilistic coupling"
    - "higher order approximate relational refinement types for mechanism design and differential privacy"
    - "proving differential privacy in hoare logic"
    - "preserving statistical validity in adaptive data analysis"
    - "calibrating data to sensitivity in private data analysis"
    - "logical metric and algorithmic characterisations of probabilistic bisimulation"

Connected through hyperedge HE-7 (induced by source paper "autopriv automating differential privacy proofs"):
  with members:
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "advanced probabilistic couplings for differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "proving differential privacy via probabilistic couplings"
    - "differentially private bayesian programming"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "proving differential privacy in hoare logic"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "understanding the sparse vector technique for differential privacy"):
  with members:
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "differentially private algorithms for empirical machine learning"
    - "calibrating data to sensitivity in private data analysis"
    - "iterative constructions and private data release"
    - "interactive privacy via the median mechanism"

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)