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: "fast private data release algorithms for sparse queries"
  Abstract: We revisit the problem of accurately answering large classes of statistical queries while preserving differential privacy. Previous approaches to this problem have either been very general but have not had run-time polynomial in the size of the database, have applied only to very limited classes of queries, or have relaxed the notion of worst-case error guarantees. In this paper we consider the large class of sparse queries, which take non-zero values on only polynomially many universe elements. We give efficient query release algorithms for this class, in both the interactive and the non-interactive setting. Our algorithms also achieve better accuracy bounds than previous general techniques do when applied to sparse queries: our bounds are independent of the universe size. In fact, even the runtime of our interactive mechanism is independent of the universe size, and so can be implemented in the "infinite universe" model in which no finite universe need be specified by the data curator.

Connected through hyperedge HE-1 (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"
    - "an improved private mechanism for small databases"

Connected through hyperedge HE-2 (induced by source paper "a differential privacy mechanism design under matrix valued query"):
  with members:
    - "towards practical differential privacy for sql queries"
    - "understanding the sparse vector technique for differential privacy"
    - "circulant matrices and differential privacy"
    - "privacy via the johnson lindenstrauss transform"
    - "dpcube differentially private histogram release through multidimensional partitioning"

Connected through hyperedge HE-3 (induced by source paper "privacy via the johnson lindenstrauss transform"):
  with members:
    - "distributed private heavy hitters"
    - "calibrating data to sensitivity in private data analysis"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "a learning theory approach to non interactive database privacy"
    - "compressive mechanism utilizing sparse representation in differential privacy"
    - "a sparse johnson lindenstrauss transform"
    - "privacy in search logs"
    - "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"
    - "accurate and efficient private release of datacubes and contingency tables"
    - "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 "distributed private heavy hitters"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "a learning theory approach to non interactive database privacy"
    - "compressive mechanism utilizing sparse representation in differential privacy"
    - "iterative constructions and private data release"
    - "privately releasing conjunctions and the statistical query barrier"
    - "differentially private combinatorial optimization"
    - "on the geometry of differential privacy"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-6 (induced by source paper "exploiting metric structure for efficient private query release"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "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"
    - "private data release via learning thresholds"
    - "iterative constructions and private data release"
    - "privately releasing conjunctions and the statistical query barrier"
    - "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.DS(Data Structures and Algorithms)