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: "efficient batch query answering under differential privacy"
  Abstract: Differential privacy is a rigorous privacy condition achieved by randomizing query answers. This paper develops efficient algorithms for answering multiple queries under differential privacy with low error. We pursue this goal by advancing a recent approach called the matrix mechanism, which generalizes standard differentially private mechanisms. This new mechanism works by first answering a different set of queries (a strategy) and then inferring the answers to the desired workload of queries. Although a few strategies are known to work well on specific workloads, finding the strategy which minimizes error on an arbitrary workload is intractable. We prove a new lower bound on the optimal error of this mechanism, and we propose an efficient algorithm that approaches this bound for a wide range of workloads.

Connected through hyperedge HE-1 (induced by source paper "accurate and efficient private release of datacubes and contingency tables"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "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"
    - "a simple and practical algorithm for differentially private data release"
    - "differential privacy via wavelet transforms"
    - "interactive privacy via the median mechanism"

Connected through hyperedge HE-2 (induced by source paper "a simple and practical algorithm for differentially private data release"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "a learning theory approach to non interactive database privacy"
    - "privately releasing conjunctions and the statistical query barrier"
    - "differential privacy via wavelet transforms"
    - "boosting the accuracy of differentially private histograms through consistency"

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)