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: "an improved private mechanism for small databases"
  Abstract: We study the problem of answering a workload of linear queries $\mathcal{Q}$, on a database of size at most $n = o(|\mathcal{Q}|)$ drawn from a universe $\mathcal{U}$ under the constraint of (approximate) differential privacy. Nikolov, Talwar, and Zhang~\cite{NTZ} proposed an efficient mechanism that, for any given $\mathcal{Q}$ and $n$, answers the queries with average error that is at most a factor polynomial in $\log |\mathcal{Q}|$ and $\log |\mathcal{U}|$ worse than the best possible. Here we improve on this guarantee and give a mechanism whose competitiveness ratio is at most polynomial in $\log n$ and $\log |\mathcal{U}|$, and has no dependence on $|\mathcal{Q}|$. Our mechanism is based on the projection mechanism of Nikolov, Talwar, and Zhang, but in place of an ad-hoc noise distribution, we use a distribution which is in a sense optimal for the projection mechanism, and analyze it using convex duality and the restricted invertibility principle.

Connected through hyperedge HE-1 (induced by source paper "the power of factorization mechanisms in local and central differential privacy"):
  with members:
    - "differentially private sql with bounded user contribution"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "towards instance optimal private query release"
    - "towards practical differential privacy for sql queries"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "faster private release of marginals on small databases"

Connected through hyperedge HE-2 (induced by source paper "the structure of optimal private tests for simple hypotheses"):
  with members:
    - "improved differentially private analysis of variance"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "test without trust optimal locally private distribution testing"
    - "locally private hypothesis testing"
    - "inspectre privately estimating the unseen"
    - "generalization for adaptively chosen estimators via stable median"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "differentially private identity and closeness testing of discrete distributions"

Connected through hyperedge HE-3 (induced by source paper "mvg mechanism differential privacy under matrix valued query"):
  with members:
    - "a differential privacy mechanism design under matrix valued query"
    - "towards practical differential privacy for sql queries"
    - "coupling random orthonormal projection with gaussian generative model for non interactive private data release"
    - "convex optimization for linear query processing under approximate differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "optimizing batch linear queries under exact and approximate differential privacy"
    - "a data and workload aware algorithm for range queries under differential privacy"

Connected through hyperedge HE-4 (induced by source paper "towards instance optimal private query release"):
  with members:
    - "collecting telemetry data privately"
    - "dp em differentially private expectation maximization"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "lower bounds for differential privacy from gaussian width"):
  with members:
    - "calibrating data to sensitivity in private data analysis"

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.DS(Data Structures and Algorithms)