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: "sharp dichotomies for regret minimization in metric spaces"
  Abstract: The Lipschitz multi-armed bandit (MAB) problem generalizes the classical multi-armed bandit problem by assuming one is given side information consisting of a priori upper bounds on the difference in expected payoff between certain pairs of strategies. Classical results of (Lai and Robbins 1985) and (Auer et al. 2002) imply a logarithmic regret bound for the Lipschitz MAB problem on finite metric spaces. Recent results on continuum-armed bandit problems and their generalizations imply lower bounds of $\sqrt{t}$, or stronger, for many infinite metric spaces such as the unit interval. Is this dichotomy universal? We prove that the answer is yes: for every metric space, the optimal regret of a Lipschitz MAB algorithm is either bounded above by any $f\in \omega(\log t)$, or bounded below by any $g\in o(\sqrt{t})$. Perhaps surprisingly, this dichotomy does not coincide with the distinction between finite and infinite metric spaces; instead it depends on whether the completion of the metric space is compact and countable. Our proof connects upper and lower bound techniques in online learning with classical topological notions such as perfect sets and the Cantor-Bendixson theorem. Among many other results, we show a similar dichotomy for the "full-feedback" (a.k.a., "best-expert") version.

Connected through hyperedge HE-1 (induced by source paper "algorithmic bidding for virtual trading in electricity markets"):
  with members:
    - "online learning in repeated auctions"

Connected through hyperedge HE-2 (induced by source paper "online learning of optimal bidding strategy in repeated multi commodity auctions"):
  with members:
    - "online learning in repeated auctions"
    - "learning in auctions regret is hard envy is easy"
    - "bandits and experts in metric spaces"
    - "learning prices for repeated auctions with strategic buyers"
    - "budget optimization for sponsored search censored learning in mdps"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-3 (induced by source paper "bandits with movement costs and adaptive pricing"):
  with members:
    - "kernel based methods for bandit convex optimization"
    - "online learning with feedback graphs beyond bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "x armed bandits"
    - "sequential item pricing for unlimited supply"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-4 (induced by source paper "bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "linear contextual bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "adaptive crowdsourcing algorithms for the bandit survey problem"
    - "knapsack based optimal policies for budget limited multi armed bandits"

Connected through hyperedge HE-5 (induced by source paper "bandits and experts in metric spaces"):
  with members:
    - "bandits with concave rewards and convex knapsacks"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandit algorithms for tree search"
    - "bandits with knapsacks"
    - "parallelizing exploration exploitation tradeoffs with gaussian process bandit optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"
    - "truthful mechanisms with implicit payment computation"

Connected through hyperedge HE-6 (induced by source paper "the best of both worlds stochastic and adversarial bandits"):
  with members:
    - "truthful mechanisms with implicit payment computation"
    - "contextual bandits with similarity information"
    - "characterizing truthful multi armed bandit mechanisms"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-7 (induced by source paper "prediction strategies without loss"):
  with members:
    - "a parameter free hedging algorithm"
    - "multi armed bandits in metric spaces"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (induced by source paper "contextual bandits with similarity information"):
  with members:
    - "bandit algorithms for tree search"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "a contextual bandit approach to personalized news article recommendation"
    - "multi armed bandits in metric spaces"
    - "bandit problems with side observations"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)