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: "efficient tracking of large classes of experts"
  Abstract: In the framework of prediction of individual sequences, sequential prediction methods are to be constructed that perform nearly as well as the best expert from a given class. We consider prediction strategies that compete with the class of switching strategies that can segment a given sequence into several blocks, and follow the advice of a different "base" expert in each block. As usual, the performance of the algorithm is measured by the regret defined as the excess loss relative to the best switching strategy selected in hindsight for the particular sequence to be predicted. In this paper we construct prediction strategies of low computational cost for the case where the set of base experts is large. In particular we provide a method that can transform any prediction algorithm $\A$ that is designed for the base class into a tracking algorithm. The resulting tracking algorithm can take advantage of the prediction performance and potential computational efficiency of $\A$ in the sense that it can be implemented with time and space complexity only $O(n^{\gamma} \ln n)$ times larger than that of $\A$, where $n$ is the time horizon and $\gamma \ge 0$ is a parameter of the algorithm. With $\A$ properly chosen, our algorithm achieves a regret bound of optimal order for $\gamma>0$, and only $O(\ln n)$ times larger than the optimal order for $\gamma=0$ for all typical regret bound types we examined. For example, for predicting binary sequences with switching parameters under the logarithmic loss, our method achieves the optimal $O(\ln n)$ regret rate with time complexity $O(n^{1+\gamma}\ln n)$ for any $\gamma\in (0,1)$.

Connected through hyperedge HE-1 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously"):
  with members:
    - "introduction to online convex optimization"
    - "adaptive regret of convex and smooth functions"
    - "adaptive online learning in dynamic environments"
    - "dynamic regret of strongly adaptive methods"
    - "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"
    - "strongly adaptive online learning"

Connected through hyperedge HE-2 (induced by source paper "decentralized online learning take benefits from others data without sharing your own to track global trend"):
  with members:
    - "introduction to online convex optimization"
    - "online machine learning in big data streams"
    - "proximal online gradient is optimum for dynamic regret"
    - "stochastic gradient push for distributed deep learning"
    - "adaptive online learning in dynamic environments"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"

Connected through hyperedge HE-3 (induced by source paper "adaptive regret of convex and smooth functions"):
  with members:
    - "introduction to online convex optimization"
    - "adaptive online learning in dynamic environments"
    - "training deep networks without learning rates through coin betting"
    - "dynamic regret of strongly adaptive methods"
    - "scale free online learning"
    - "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"

Connected through hyperedge HE-4 (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"
    - "metagrad multiple learning rates in online learning"
    - "strongly adaptive online learning"

Connected through hyperedge HE-5 (induced by source paper "proximal online gradient is optimum for dynamic regret"):
  with members:
    - "introduction to online convex optimization"
    - "adaptive online learning in dynamic environments"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "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 learning to rank with feedback at the top"
    - "online optimization competing with dynamic comparators"

Connected through hyperedge HE-6 (induced by source paper "learning to bid without knowing your value"):
  with members:
    - "bandits with movement costs and adaptive pricing"
    - "revenue optimization with approximate bid predictions"
    - "online learning with feedback graphs without the graphs"
    - "the sample complexity of revenue maximization"
    - "learning and efficiency in games with dynamic population"
    - "online learning in repeated auctions"
    - "online learning with feedback graphs beyond bandits"
    - "online learning and profit maximization from revealed preferences"

Connected through hyperedge HE-7 (induced by source paper "efficient algorithms for adversarial contextual learning"):
  with members:
    - "bistro an efficient relaxation based method for contextual bandits"
    - "fast convergence of regularized learning in games"
    - "learning in auctions regret is hard envy is easy"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "optimization learning and games with predictable sequences"
    - "an efficient algorithm for learning with semi bandit feedback"
    - "efficient optimal learning for contextual bandits"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-8 (induced by source paper "compress and control"):
  with members:
    - "the arcade learning environment an evaluation platform for general agents"
    - "sparse adaptive dirichlet multinomial like processes"
    - "playing atari with deep reinforcement learning"
    - "learning is planning near bayes optimal reinforcement learning via monte carlo tree search"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "partition tree weighting"
    - "reinforcement learning via aixi approximation"
    - "a monte carlo aixi approximation"

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)