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 "lipschitz adaptivity with multiple learning rates in online learning"):
  Abstract: We aim to design adaptive online learning algorithms that take advantage of any special structure that might be present in the learning task at hand, with as little manual tuning by the user as possible. A fundamental obstacle that comes up in the design of such adaptive algorithms is to calibrate a so-called step-size or learning rate hyperparameter depending on variance, gradient norms, etc. A recent technique promises to overcome this difficulty by maintaining multiple learning rates in parallel. This technique has been applied in the MetaGrad algorithm for online convex optimization and the Squint algorithm for prediction with expert advice. However, in both cases the user still has to provide in advance a Lipschitz hyperparameter that bounds the norm of the gradients. Although this hyperparameter is typically not available in advance, tuning it correctly is crucial: if it is set too small, the methods may fail completely; but if it is taken too large, performance deteriorates significantly. In the present work we remove this Lipschitz hyperparameter by designing new versions of MetaGrad and Squint that adapt to its optimal value automatically. We achieve this by dynamically updating the set of active learning rates. For MetaGrad, we further improve the computational efficiency of handling constraints on the domain of prediction, and we remove the need to specify the number of rounds in advance.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "black box reductions for parameter free online learning in banach spaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive online learning with varying norms")
  Member "zigzag a new approach to adaptive online learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lipschitz and comparator norm adaptivity in online learning")
  Member "online learning without prior information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive online learning with varying norms")
  Member "metagrad multiple learning rates in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lipschitz and comparator norm adaptivity in online learning")
  Member "combining adversarial guarantees and stochastic fast rates in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast rates for online prediction with abstention")
  Member "second order quantile methods for experts and combinatorial games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a modern introduction to online learning")
  Member "achieving all with no parameters adaptive normalhedge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "competitive ratio versus regret minimization achieving the best of both worlds")

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)