human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "metagrad multiple learning rates in online learning"
  Abstract: In online convex optimization it is well known that certain subclasses of objective functions are much easier than arbitrary convex functions. We are interested in designing adaptive methods that can automatically get fast rates in as many such subclasses as possible, without any manual tuning. Previous adaptive methods are able to interpolate between strongly convex and general convex functions. We present a new method, MetaGrad, that adapts to a much broader class of functions, including exp-concave and strongly convex functions, but also various types of stochastic and non-stochastic functions without any curvature. For instance, MetaGrad can achieve logarithmic regret on the unregularized hinge loss, even though it has no curvature, if the data come from a favourable probability distribution. MetaGrad's main feature is that it simultaneously considers multiple learning rates. Unlike previous methods with provable regret guarantees, however, its learning rates are not monotonically decreasing over time and are not tuned based on a theoretically derived bound on the regret. Instead, they are weighted directly proportional to their empirical performance on the data using a tilted exponential weights master algorithm.

Connected through hyperedge HE-1 (induced by source paper "lipschitz and comparator norm adaptivity in online learning"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "introduction to online convex optimization"
    - "lipschitz adaptivity with multiple learning rates in online learning"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"
    - "zigzag a new approach to adaptive online learning"
    - "online learning without prior information"

Connected through hyperedge HE-2 (induced by source paper "locally adaptive nonparametric online learning"):
  with members:
    - "introduction to online convex optimization"
    - "gated linear networks"
    - "lipschitz adaptivity with multiple learning rates in online learning"
    - "nonparametric online regression while learning the metric"
    - "coin betting and parameter free online learning"
    - "online learning via sequential complexities"
    - "metric entropy in competitive on line prediction"
    - "competing with wild prediction rules"

Connected through hyperedge HE-3 (induced by source paper "lipschitz adaptivity with multiple learning rates in online learning"):
  with members:
    - "introduction to online convex optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "zigzag a new approach to adaptive online learning"
    - "online learning without prior information"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "achieving all with no parameters adaptive normalhedge"
    - "prediction with advice of unknown number of experts"

Connected through hyperedge HE-4 (induced by source paper "adaptive online learning for gradient based optimizers"):
  with members:
    - "exponentiated gradient meets gradient descent"
    - "introduction to online convex optimization"
    - "a tutorial on thompson sampling"
    - "online learning without prior information"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "online optimization competing with dynamic comparators"
    - "fighting bandits with a new kind of smoothness"

Connected through hyperedge HE-5 (induced by source paper "dual adaptivity a universal algorithm for minimizing the adaptive regret of convex functions"):
  with members:
    - "adaptivity and optimality a universal algorithm for online convex optimization"
    - "introduction to online convex optimization"
    - "adaptive regret of convex and smooth functions"
    - "dynamic regret of strongly adaptive methods"
    - "strongly adaptive online learning"
    - "efficient tracking of large classes of experts"

Connected through hyperedge HE-6 (induced by source paper "surrogate losses for online learning of stepsizes in stochastic non convex optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "online learning rate adaptation with hypergradient descent"
    - "linear convergence of gradient and proximal gradient methods under the polyak l ojasiewicz condition"
    - "adam a method for stochastic optimization"
    - "a survey of algorithms and analysis for adaptive online learning"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "adaptivity and optimality a universal algorithm for online convex optimization"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "adaptive online learning in dynamic environments"
    - "strongly adaptive online learning"
    - "a stochastic view of optimal regret through minimax duality"

Connected through hyperedge HE-8 (induced by source paper "learning the best expert efficiently"):
  with members:
    - "corralling a band of bandit algorithms"
    - "second order quantile methods for experts and combinatorial games"

Question: Please tell me which class the center node 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)