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 "simple and fast algorithm for binary integer and online linear programming"):
  Abstract: In this paper, we develop a simple and fast online algorithm for solving a general class of binary integer linear programs (LPs). The algorithm requires only one single pass through the input data and is free of doing any matrix inversion. It can be viewed as both an approximate algorithm for solving binary integer LPs and a fast algorithm for solving online LP problems. The algorithm is inspired by an equivalent form of the dual problem of the relaxed LP and it essentially performs projected stochastic subgradient descent in the dual space. We analyze the algorithm in two different models, stochastic input model and random permutation model, with minimal assumptions on the input of the LP. The algorithm achieves $O\left(m^2 \sqrt{n}\right)$ expected regret under the stochastic input model and $O\left((m^2+\log n)\sqrt{n}\right)$ expected regret under the random permutation model, and it achieves $O(m \sqrt{n})$ expected constraint violation under both models, where $n$ is the number of decision variables and $m$ is the number of constraints. Furthermore, the algorithm is generalized to a multi-dimensional LP setting which covers a wider range of applications and features for the same performance guarantee. Numerical experiments illustrate the general applicability and the performance of the algorithms.

Includes members:
  Member "online linear programming dual convergence new algorithms and regret bounds":
    also connected through other hyperedges:
      (none)
  Member "how good is sgd with random shuffling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  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 "online convex optimization for cumulative constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed online optimization with long term constraints")
  Member "a linearly relaxed approximate linear program for markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a reduction from reinforcement learning to no regret online learning")
  Member "fast algorithms for online stochastic convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "forming diverse teams from sequentially arriving people")
  Member "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting sgd with increasingly weighted averaging optimization and generalization perspectives")
  Member "trading regret for efficiency online convex optimization with long term constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploration exploitation in constrained mdps")

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)