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: "the computational power of optimization in online learning"
  Abstract: We consider the fundamental problem of prediction with expert advice where the experts are "optimizable": there is a black-box optimization oracle that can be used to compute, in constant time, the leading expert in retrospect at any point in time. In this setting, we give a novel online algorithm that attains vanishing regret with respect to $N$ experts in total $\widetilde{O}(\sqrt{N})$ computation time. We also give a lower bound showing that this running time cannot be improved (up to log factors) in the oracle model, thereby exhibiting a quadratic speedup as compared to the standard, oracle-free setting where the required time for vanishing regret is $\widetilde{\Theta}(N)$. These results demonstrate an exponential gap between the power of optimization in online learning and its power in statistical learning: in the latter, an optimization oracle---i.e., an efficient empirical risk minimizer---allows to learn a finite hypothesis class of size $N$ in time $O(\log{N})$. We also study the implications of our results to learning in repeated zero-sum games, in a setting where the players have access to oracles that compute, in constant time, their best-response to any mixed strategy of their opponent. We show that the runtime required for approximating the minimax value of the game in this setting is $\widetilde{\Theta}(\sqrt{N})$, yielding again a quadratic improvement upon the oracle-free setting, where $\widetilde{\Theta}(N)$ is known to be tight.

Connected through hyperedge HE-1 (induced by source paper "taking a hint how to leverage loss predictors in contextual bandits"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "improved path length regret bounds for bandits"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-2 (induced by source paper "competitive ratio versus regret minimization achieving the best of both worlds"):
  with members:
    - "online learning over a finite action set with limited switching"
    - "k server via multiscale entropic regularization"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"
    - "online optimization competing with dynamic comparators"
    - "strongly adaptive online learning"
    - "achieving all with no parameters adaptive normalhedge"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "optimal amortized regret in every interval"

Connected through hyperedge HE-3 (induced by source paper "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"):
  with members:
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "online optimization competing with dynamic comparators"

Connected through hyperedge HE-4 (induced by source paper "online learning for min max discrete problems"):
  with members:
    - "introduction to online convex optimization"
    - "learning in non convex games with an optimization oracle"
    - "efficient online linear optimization with approximation algorithms"
    - "oracle efficient online learning and auction design"
    - "efficient algorithms for adversarial contextual learning"
    - "learning in auctions regret is hard envy is easy"
    - "secretary problems with convex costs"

Connected through hyperedge HE-5 (induced by source paper "how to use heuristics for differential privacy"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "model agnostic private learning via stability"
    - "property testing for differential privacy"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "online learning via differential privacy"
    - "adaptive learning with robust generalization guarantees"
    - "efficient algorithms for adversarial contextual learning"

Connected through hyperedge HE-6 (induced by source paper "learning in non convex games with an optimization oracle"):
  with members:
    - "introduction to online convex optimization"
    - "on convergence and stability of gans"
    - "efficient regret minimization in non convex games"
    - "fast rates for empirical risk minimization of strict saddle problems"
    - "oracle efficient online learning and auction design"
    - "prediction by random walk perturbation"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"

Connected through hyperedge HE-7 (induced by source paper "efficient contextual bandits in non stationary worlds"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "strongly adaptive regret implies optimally dynamic regret"
    - "corralling a band of bandit algorithms"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"

Connected through hyperedge HE-8 (induced by source paper "oracle efficient online learning and auction design"):
  with members:
    - "online learning with low rank experts"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "learning in auctions regret is hard envy is easy"

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)