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 "an equivalence between private classification and online prediction"):
  Abstract: We prove that every concept class with finite Littlestone dimension can be learned by an (approximate) differentially-private algorithm. This answers an open question of Alon et al. (STOC 2019) who proved the converse statement (this question was also asked by Neel et al.~(FOCS 2019)). Together these two results yield an equivalence between online learnability and private PAC learnability. #R##N#We introduce a new notion of algorithmic stability called "global stability" which is essential to our proof and may be of independent interest. We also discuss an application of our results to boosting the privacy and accuracy parameters of differentially-private learners.

Includes members:
  Member "efficient noise tolerant and private learning via boosting":
    also connected through other hyperedges:
      (none)
  Member "a necessary and sufficient stability notion for adaptive generalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a new analysis of differential privacy s generalization guarantees")
  Member "passing tests without memorizing two models for fooling discriminators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online agnostic boosting via regret minimization")
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "privately learning thresholds closing the exponential gap":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "closure properties for private classification and online prediction")
  Member "privately answering classification queries in the agnostic pac model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pac learning with stable and private predictions")
  Member "the role of interactivity in local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "locally private hypothesis selection")
  Member "private learning implies online learning an efficient reduction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online agnostic boosting via regret minimization")

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)