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 "differentially private online learning"):
  Abstract: In this paper, we consider the problem of preserving privacy in the online learning setting. We study the problem in the online convex programming (OCP) framework---a popular online learning setting with several interesting theoretical and practical implications---while using differential privacy as the formal privacy measure. For this problem, we distill two critical attributes that a private OCP algorithm should have in order to provide reasonable privacy as well as utility guarantees: 1) linearly decreasing sensitivity, i.e., as new data points arrive their effect on the learning model decreases, 2) sub-linear regret bound---regret bound is a popular goodness/utility measure of an online learning algorithm. #R##N#Given an OCP algorithm that satisfies these two conditions, we provide a general framework to convert the given algorithm into a privacy preserving OCP algorithm with good (sub-linear) regret. We then illustrate our approach by converting two popular online learning algorithms into their differentially private variants while guaranteeing sub-linear regret ($O(\sqrt{T})$). Next, we consider the special case of online linear regression problems, a practically important class of online learning problems, for which we generalize an approach by Dwork et al. to provide a differentially private algorithm with just $O(\log^{1.5} T)$ regret. Finally, we show that our online learning framework can be used to provide differentially private algorithms for offline learning as well. For the offline learning problem, our approach obtains better error bounds as well as can handle larger class of problems than the existing state-of-the-art methods Chaudhuri et al.

Includes members:
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally private hypothesis selection")
  Member "online learning stability and stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream")
  Member "a learning theory approach to non interactive database privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "lower bounds in differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "iterative constructions and private data release":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "data dependent differentially private parameter learning for directed graphical models")
  Member "stability conditions for online learnability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online learning via the differential privacy lens")
  Member "a simple and practical algorithm for differentially private data release":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "on the geometry of differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privately learning markov random fields")

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.LG(Machine Learning)