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: "achieving all with no parameters adaptive normalhedge"
  Abstract: We study the classic online learning problem of predicting with expert advice, and propose a truly parameter-free and adaptive algorithm that achieves several objectives simultaneously without using any prior information. The main component of this work is an improved version of the NormalHedge.DT algorithm (Luo and Schapire, 2014), called AdaNormalHedge. On one hand, this new algorithm ensures small regret when the competitor has small loss and almost constant regret when the losses are stochastic. On the other hand, the algorithm is able to compete with any convex combination of the experts simultaneously, with a regret in terms of the relative entropy of the prior and the competitor. This resolves an open problem proposed by Chaudhuri et al. (2009) and Chernov and Vovk (2010). Moreover, we extend the results to the sleeping expert setting and provide two applications to illustrate the power of AdaNormalHedge: 1) competing with time-varying unknown competitors and 2) predicting almost as well as the best pruning tree. Our results on these applications significantly improve previous work from different aspects, and a special case of the first application resolves another open problem proposed by Warmuth and Koolen (2014) on whether one can simultaneously achieve optimal shifting regret for both adversarial and stochastic losses.

Connected through hyperedge HE-1 (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"
    - "the computational power of optimization in online learning"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "optimal amortized regret in every interval"

Connected through hyperedge HE-2 (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"
    - "metagrad multiple learning rates in online learning"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "prediction with advice of unknown number of experts"

Connected through hyperedge HE-3 (induced by source paper "combining adversarial guarantees and stochastic fast rates in online learning"):
  with members:
    - "metagrad multiple learning rates in online learning"
    - "fast rates in statistical and online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "from stochastic mixability to fast rates"
    - "follow the leader if you can hedge if you must"
    - "adaptive bound optimization for online convex optimization"

Connected through hyperedge HE-4 (induced by source paper "learning and efficiency in games with dynamic population"):
  with members:
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "inducing approximately optimal flow using truthful mediators"
    - "private pareto optimal exchange"
    - "mirror descent meets fixed share and feels no regret"
    - "bayesian games and the smoothness framework"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-5 (induced by source paper "second order quantile methods for experts and combinatorial games"):
  with members:
    - "a drifting games analysis for online learning and applications to boosting"
    - "prediction with advice of unknown number of experts"
    - "online linear optimization via smoothing"
    - "follow the leader if you can hedge if you must"
    - "regret in online combinatorial optimization"
    - "a parameter free hedging algorithm"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-6 (induced by source paper "adaptive online learning"):
  with members:
    - "second order quantile methods for experts and combinatorial games"
    - "learning without concentration"
    - "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations"
    - "a parameter free hedging algorithm"

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)