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 tale of two metrics simultaneous bounds on competitiveness and regret"
  Abstract: We consider algorithms for "smoothed online convex optimization" problems, a variant of the class of online convex optimization problems that is strongly related to metrical task systems. Prior literature on these problems has focused on two performance metrics: regret and the competitive ratio. There exist known algorithms with sublinear regret and known algorithms with constant competitive ratios; however, no known algorithm achieves both simultaneously. We show that this is due to a fundamental incompatibility between these two metrics - no algorithm (deterministic or randomized) can achieve sublinear regret and a constant competitive ratio, even in the case when the objective functions are linear. However, we also exhibit an algorithm that, for the important special case of one-dimensional decision spaces, provides sublinear regret while maintaining a competitive ratio that grows arbitrarily slowly.

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"
    - "online optimization competing with dynamic comparators"
    - "strongly adaptive online learning"
    - "the computational power of optimization in 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-2 (induced by source paper "understand dynamic regret with switching cost for online decision making"):
  with members:
    - "introduction to online convex optimization"
    - "proximal online gradient is optimum for dynamic regret"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "metrical task systems on trees via mirror descent and unfair gluing"
    - "adaptive online learning in dynamic environments"
    - "dynamic regret of strongly adaptive methods"
    - "fusible hsts and the randomized k server conjecture"
    - "k server via multiscale entropic regularization"

Connected through hyperedge HE-3 (induced by source paper "online optimization with predictions and non convex losses"):
  with members:
    - "chasing convex bodies optimally"
    - "introduction to online convex optimization"
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "improved path length regret bounds for bandits"
    - "a nearly linear bound for chasing nested convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "chasing nested convex bodies nearly optimally"
    - "potential function proofs for first order methods"

Connected through hyperedge HE-4 (induced by source paper "minimax regret of switching constrained online convex optimization no phase transition"):
  with members:
    - "online learning over a finite action set with limited switching"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "an optimal algorithm for bandit convex optimization"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "prediction by random walk perturbation"
    - "bandits with switching costs t 2 3 regret"
    - "online learning with switching costs and other adaptive adversaries"
    - "online bandit learning against an adaptive adversary from regret to policy regret"

Connected through hyperedge HE-5 (induced by source paper "competitive online optimization under inventory constraints"):
  with members:
    - "balancing cost and dissatisfaction in online ev charging under real time pricing"
    - "nested convex bodies are chaseable"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "online offering strategies for storage assisted renewable power producer in hour ahead market"
    - "online convex optimization using predictions"
    - "peak aware online economic dispatching for microgrids"
    - "the submodular secretary problem goes linear"
    - "online energy generation scheduling for microgrids with intermittent energy sources and co generation"

Connected through hyperedge HE-6 (induced by source paper "smoothed online optimization for regression and control"):
  with members:
    - "introduction to online convex optimization"
    - "a nearly linear bound for chasing nested convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "online convex optimization using predictions"

Connected through hyperedge HE-7 (induced by source paper "smoothed online convex optimization in high dimensions via online balanced descent"):
  with members:
    - "introduction to online convex optimization"
    - "online convex optimization using predictions"

Connected through hyperedge HE-8 (induced by source paper "optimal algorithms for right sizing data centers extended version"):
  with members:
    - "characterizing the impact of the workload on the value of dynamic resizing in data centers"

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.DS(Data Structures and Algorithms)