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: "accurate and efficient private release of datacubes and contingency tables"
  Abstract: A central problem in releasing aggregate information about sensitive data is to do so accurately while providing a privacy guarantee on the output. Recent work focuses on the class of linear queries, which include basic counting queries, data cubes, and contingency tables. The goal is to maximize the utility of their output, while giving a rigorous privacy guarantee. Most results follow a common template: pick a "strategy" set of linear queries to apply to the data, then use the noisy answers to these queries to reconstruct the queries of interest. This entails either picking a strategy set that is hoped to be good for the queries, or performing a costly search over the space of all possible strategies. #R##N#In this paper, we propose a new approach that balances accuracy and efficiency: we show how to improve the accuracy of a given query set by answering some strategy queries more accurately than others. This leads to an efficient optimal noise allocation for many popular strategies, including wavelets, hierarchies, Fourier coefficients and more. For the important case of marginal queries we show that this strictly improves on previous methods, both analytically and empirically. Our results also extend to ensuring that the returned query answers are consistent with an (unknown) data set at minimal extra cost in terms of time and noise.

Connected through hyperedge HE-1 (induced by source paper "privtree a differentially private algorithm for hierarchical decompositions"):
  with members:
    - "on the privacy properties of variants on the sparse vector technique"
    - "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"
    - "optimal error of query sets under the differentially private matrix mechanism"
    - "differentially private grids for geospatial data"

Connected through hyperedge HE-2 (induced by source paper "dual query practical private query release for high dimensional data"):
  with members:
    - "characterizing the sample complexity of private learners"
    - "differential privacy for the analyst via private equilibrium computation"
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "faster algorithms for privately releasing marginals"
    - "answering n 2 o 1 counting queries with differential privacy is hard"
    - "a learning theory approach to non interactive database privacy"
    - "iterative constructions and private data release"

Connected through hyperedge HE-3 (induced by source paper "a data and workload aware algorithm for range queries under differential privacy"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "low rank mechanism for optimizing batch queries under differential privacy"
    - "differentially private spatial decompositions"
    - "iterative constructions and private data release"
    - "a simple and practical algorithm for differentially private data release"
    - "differential privacy via wavelet transforms"
    - "boosting the accuracy of differentially private histograms through consistency"

Connected through hyperedge HE-4 (induced by source paper "non interactive differential privacy a survey"):
  with members:
    - "small count privacy and large count utility in data publishing"
    - "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"
    - "fast private data release algorithms for sparse queries"
    - "differentially private search log sanitization with optimal output utility"
    - "a learning theory approach to non interactive database privacy"
    - "compressive mechanism utilizing sparse representation in differential privacy"

Connected through hyperedge HE-5 (induced by source paper "optimal error of query sets under the differentially private matrix mechanism"):
  with members:
    - "the geometry of differential privacy the sparse and approximate cases"
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "low rank mechanism for optimizing batch queries under differential privacy"
    - "a learning theory approach to non interactive database privacy"
    - "differentially private spatial decompositions"
    - "compressive mechanism utilizing sparse representation in differential privacy"
    - "iterative constructions and private data release"

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)