human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "online linear programming dual convergence new algorithms and regret bounds"):
  Abstract: We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the objective function are generated i.i.d. from an unknown distribution and revealed sequentially over time. Virtually all current online algorithms were based on learning the dual optimal solutions/prices of the linear programs (LP), and their analyses were focused on the aggregate objective value and solving the packing LP where all coefficients in the constraint matrix and objective are nonnegative. However, two major open questions are: (i) Does the set of LP optimal dual prices in OLP converge to those of the "offline" LP, and (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative. We resolve these two questions by establishing convergence results for the dual prices under moderate regularity conditions for general LP problems. Then we propose a new type of OLP algorithm, Action-History-Dependent Learning Algorithm, which improves the previous algorithm performances by taking into account the past input data as well as and decisions/actions already made. We derive an $O(\log n \log \log n)$ regret bound for the proposed algorithm, against the $O(\sqrt{n})$ bound for typical dual-price learning algorithms, and show that no dual-based thresholding algorithm achieves a worst-case regret smaller than $O(\log n)$, where n is the number of decision variables. Numerical experiments demonstrate the superior performance of the proposed algorithms and the effectiveness of our action-history-dependent design. Our results also indicate that, for solving online optimization problems with constraints, it's better to utilize a non-stationary policy rather than the stationary one.

Includes members:
  Member "near optimal online algorithms and fast approximation algorithms for resource allocation problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "a primal dual learning algorithm for personalized dynamic pricing with an inventory constraint":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nonparametric pricing analytics with customer covariates")
  Member "algorithms with logarithmic or sublinear regret for constrained contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem")
  Member "fast algorithms for online stochastic convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "simple and fast algorithm for binary integer and online linear programming")
  Member "a dynamic near optimal algorithm for online linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random order models")

Question: Please tell me which class the source paper 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)