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: "low rank mechanism for optimizing batch queries under differential privacy"
  Abstract: Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results, while satisfying the privacy guarantees. Previous work, notably \cite{LHR+10}, has suggested that with an appropriate strategy, processing a batch of correlated queries as a whole achieves considerably higher accuracy than answering them individually. However, to our knowledge there is currently no practical solution to find such a strategy for an arbitrary query batch; existing methods either return strategies of poor quality (often worse than naive methods) or require prohibitively expensive computations for even moderately large domains. #R##N#Motivated by this, we propose the \emph{Low-Rank Mechanism} (LRM), the first practical differentially private technique for answering batch queries with high accuracy, based on a \emph{low rank approximation} of the workload matrix. We prove that the accuracy provided by LRM is close to the theoretical lower bound for any mechanism to answer a batch of queries under differential privacy. Extensive experiments using real data demonstrate that LRM consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.

Connected through hyperedge HE-1 (induced by source paper "convex optimization for linear query processing under approximate differential privacy"):
  with members:
    - "optimizing batch linear queries under exact and approximate differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "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"
    - "optimal error of query sets under the differentially private matrix mechanism"
    - "differential privacy via wavelet transforms"

Connected through hyperedge HE-2 (induced by source paper "optimizing batch linear queries under exact and approximate differential privacy"):
  with members:
    - "the geometry of differential privacy the sparse and approximate cases"
    - "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"
    - "beating randomized response on incoherent matrices"
    - "optimal error of query sets under the differentially private matrix mechanism"
    - "functional mechanism regression analysis under differential privacy"
    - "a learning theory approach to non interactive database privacy"

Connected through hyperedge HE-3 (induced by source paper "principled evaluation of differentially private algorithms using dpbench"):
  with members:
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "differentially private grids for geospatial data"
    - "differentially private spatial decompositions"
    - "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 "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"
    - "accurate and efficient private release of datacubes and contingency tables"
    - "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-5 (induced by source paper "a theory of pricing private data"):
  with members:
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "a market for unbiased private data paying individuals according to their privacy attitudes"
    - "calibrating data to sensitivity in private data analysis"
    - "conducting truthful surveys cheaply"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "truthful mechanisms for agents that value privacy"

Connected through hyperedge HE-6 (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"
    - "accurate and efficient private release of datacubes and contingency tables"
    - "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)