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 "metagrad faster convergence without curvature in online convex optimization"):
  Abstract: In online convex optimization it is well known that objective functions with curvature are much easier than arbitrary convex functions. Here we show that the regret can be significantly reduced even without curvature, in cases where there is a stable optimum to converge to. More precisely, the regret of existing methods is determined by the norms of the encountered gradients, and matching worst-case performance lower bounds tell us that this cannot be improved uniformly. Yet we argue that this is a rather pessimistic assessment of the complexity of the problem. We introduce a new parameter-free algorithm, called MetaGrad, for which the gradient norms in the regret are scaled down by the distance to the (unknown) optimum. So when the optimum is reasonably stable over time, making the algorithm converge, this new scaling leads to orders of magnitude smaller regret even when the gradients themselves do not vanish. MetaGrad does not require any manual tuning, but instead tunes a learning rate parameter automatically for the data. Unlike all previous methods with provable guarantees, its learning rates are not monotonically decreasing over time, but instead are based on a novel aggregation technique. We provide two versions of MetaGrad. The first maintains a full covariance matrix to guarantee the sharpest bounds for problems where we can afford update time quadratic in the dimension. The second version maintains only the diagonal. Its linear cost in the dimension makes it suitable for large-scale problems.

Includes members:
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks")
  Member "second order quantile methods for experts and combinatorial games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a modern introduction to online learning")
  Member "prediction with advice of unknown number of experts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a modern introduction to online learning")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "adadelta an adaptive learning rate method":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "making gradient descent optimal for strongly convex stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wireless federated learning with local differential privacy")
  Member "supermartingales in prediction with expert advice":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "metagrad multiple learning rates in online learning")

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)