human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "private multiplicative weights beyond linear queries"):
  Abstract: A wide variety of fundamental data analyses in machine learning, such as linear and logistic regression, require minimizing a convex function defined by the data. Since the data may contain sensitive information about individuals, and these analyses can leak that sensitive information, it is important to be able to solve convex minimization in a privacy-preserving way. #R##N#A series of recent results show how to accurately solve a single convex minimization problem in a differentially private manner. However, the same data is often analyzed repeatedly, and little is known about solving multiple convex minimization problems with differential privacy. For simpler data analyses, such as linear queries, there are remarkable differentially private algorithms such as the private multiplicative weights mechanism (Hardt and Rothblum, FOCS 2010) that accurately answer exponentially many distinct queries. In this work, we extend these results to the case of convex minimization and show how to give accurate and differentially private solutions to *exponentially many* convex minimization problems on a sensitive dataset.

Includes members:
  Member "more general queries and less generalization error in adaptive data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic stability for adaptive data analysis")
  Member "preventing false discovery in interactive data analysis is hard":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "private learning and sanitization pure vs approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an equivalence between private classification and online prediction")
  Member "preserving statistical validity in adaptive data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "faster private release of marginals on small databases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the power of factorization mechanisms in local and central differential privacy")
  Member "efficient algorithms for privately releasing marginals via convex relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the power of factorization mechanisms in local and central differential privacy")
  Member "fingerprinting codes and the price of approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privately learning markov random fields")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "locally private hypothesis selection")

Question: Please tell me which class the source paper 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)