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: "a survey of algorithms and analysis for adaptive online learning"
  Abstract: We present tools for the analysis of Follow-The-Regularized-Leader (FTRL), Dual Averaging, and Mirror Descent algorithms when the regularizer (equivalently, prox-function or learning rate schedule) is chosen adaptively based on the data. Adaptivity can be used to prove regret bounds that hold on every round, and also allows for data-dependent regret bounds as in AdaGrad-style algorithms (e.g., Online Gradient Descent with adaptive per-coordinate learning rates). We present results from a large number of prior works in a unified manner, using a modular and tight analysis that isolates the key arguments in easily re-usable lemmas. This approach strengthens pre-viously known FTRL analysis techniques to produce bounds as tight as those achieved by potential functions or primal-dual analysis. Further, we prove a general and exact equivalence between an arbitrary adaptive Mirror Descent algorithm and a correspond- ing FTRL update, which allows us to analyze any Mirror Descent algorithm in the same framework. The key to bridging the gap between Dual Averaging and Mirror Descent algorithms lies in an analysis of the FTRL-Proximal algorithm family. Our regret bounds are proved in the most general form, holding for arbitrary norms and non-smooth regularizers with time-varying weight.

Connected through hyperedge HE-1 (induced by source paper "adaptive online learning with varying norms"):
  with members:
    - "extreme tensoring for low memory preconditioning"
    - "adaptive scale invariant online algorithms for learning linear models"
    - "parameter free online convex optimization with sub exponential noise"
    - "matrix free preconditioning in online learning"
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"

Connected through hyperedge HE-2 (induced by source paper "provably efficient adaptive approximate policy iteration"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "worst case regret bounds for exploration via randomized value functions"
    - "provably efficient exploration in policy optimization"
    - "a theory of regularized markov decision processes"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"

Connected through hyperedge HE-3 (induced by source paper "logistic regression regret what s the catch"):
  with members:
    - "logistic regression tight bounds for stochastic and online optimization"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-4 (induced by source paper "a modern introduction to online learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "black box reductions for parameter free online learning in banach spaces"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "training deep networks without learning rates through coin betting"
    - "scale free online learning"
    - "coin betting and parameter free online learning"
    - "fighting bandits with a new kind of smoothness"
    - "second order quantile methods for experts and combinatorial games"

Connected through hyperedge HE-5 (induced by source paper "matrix free preconditioning in online learning"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "the case for full matrix adaptive regularization"
    - "attention is all you need"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "efficient second order online learning by sketching"

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"
    - "metagrad multiple learning rates in online learning"
    - "linear convergence of gradient and proximal gradient methods under the polyak l ojasiewicz condition"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "learning effective loss functions efficiently"):
  with members:
    - "learning optimal linear regularizers"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "solving linear programs in the current matrix multiplication time"
    - "imagenet large scale visual recognition challenge"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-8 (induced by source paper "predictor corrector policy optimization"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "universal planning networks"
    - "dual policy iteration"
    - "sim to real learning agile locomotion for quadruped robots"
    - "stochastic variance reduced policy gradient"
    - "convergence of value aggregation for imitation learning"
    - "accelerating imitation learning with predictive models"

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)