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 "curvature of feasible sets in offline and online optimization"):
  Abstract: It is known that the curvature of the feasible set in convex optimization allows for algorithms with better convergence rates, and there has been renewed interest in this topic both for offline as well as online problems. #R##N#In this paper, leveraging results on geometry and convex analysis, we further our understanding of the role of curvature in optimization: #R##N#- We first show the equivalence of two notions of curvature, namely strong convexity and gauge bodies, proving a conjecture of Abernethy et al. As a consequence, this show that the Frank-Wolfe-type method of Wang and Abernethy has accelerated convergence rate $O(\frac{1}{t^2})$ over strongly convex feasible sets without additional assumptions on the (convex) objective function. #R##N#- In Online Linear Optimization, we show that the logarithmic regret guarantee of the algorithm Follow the Leader (FTL) over strongly convex sets recently proved by Huang et al. follows directly from a partial lipschitzness of the support function of such sets. We believe that this provides a simpler explanation for the good performance of FTL in this context. #R##N#- We provide an efficient procedure for approximating convex bodies by curved ones, smoothly trading off approximation error and curvature, allowing one to extend the applicability of algorithms for curved set to non-curved ones.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "acceleration through optimistic no regret dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stable predictive optimistic counterfactual regret minimization")
  Member "faster rates for convex concave games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization")
  Member "parameter free online learning via model selection":
    also connected through other hyperedges:
      (none)
  Member "minding the gaps for block frank wolfe optimization of structured svms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a flexible model for training action localization with varying levels of supervision")
  Member "block coordinate frank wolfe optimization for structural svms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "on the universality of online mirror descent":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")

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)