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 hypothesis selection"):
  Abstract: We provide a differentially private algorithm for hypothesis selection. Given samples from an unknown probability distribution $P$ and a set of $m$ probability distributions $\mathcal{H}$, the goal is to output, in a $\varepsilon$-differentially private manner, a distribution from $\mathcal{H}$ whose total variation distance to $P$ is comparable to that of the best such distribution (which we denote by $\alpha$). The sample complexity of our basic algorithm is $O\left(\frac{\log m}{\alpha^2} + \frac{\log m}{\alpha \varepsilon}\right)$, representing a minimal cost for privacy when compared to the non-private algorithm. We also can handle infinite hypothesis classes $\mathcal{H}$ by relaxing to $(\varepsilon,\delta)$-differential privacy. #R##N#We apply our hypothesis selection algorithm to give learning algorithms for a number of natural distribution classes, including Gaussians, product distributions, sums of independent random variables, piecewise polynomials, and mixture classes. Our hypothesis selection procedure allows us to generically convert a cover for a class to a learning algorithm, complementing known learning lower bounds which are in terms of the size of the packing number of the class. As the covering and packing numbers are often closely related, for constant $\alpha$, our algorithms achieve the optimal sample complexity for many classes of interest. Finally, we describe an application to private distribution-free PAC learning.

Includes members:
  Member "the optimal approximation factor in density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally private hypothesis selection")
  Member "differentially private algorithms for learning mixtures of separated gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "private mean estimation of heavy tailed distributions")
  Member "hadamard response estimating distributions privately efficiently and with little communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "privately learning high dimensional distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locally private hypothesis selection")
  Member "locally private gaussian estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private mean estimation of heavy tailed distributions")
  Member "private pac learning implies finite littlestone dimension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an equivalence between private classification and online prediction")
  Member "learning sums of independent random variables with sparse collective support":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "density estimation for shift invariant multidimensional distributions")
  Member "inspectre privately estimating the unseen":
    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)