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 primal dual algorithms with configuration linear programs"):
  Abstract: Non-linear, especially convex, objective functions have been extensively studied in recent years in which approaches relies crucially on the convexity property of cost functions. In this paper, we present primal-dual approaches based on configuration linear programs to design competitive online algorithms for problems with arbitrarily-grown objective. This approach is particularly appropriate for non-linear (non-convex) objectives in online setting. #R##N#We first present a simple greedy algorithm for a general cost-minimization problem. The competitive ratio of the algorithm is characterized by the mean of a notion, called smoothness, which is inspired by a similar concept in the context of algorithmic game theory. The algorithm gives optimal (up to a constant factor) competitive ratios while applying to different contexts such as network routing, vector scheduling, energy-efficient scheduling and non-convex facility location. #R##N#Next, we consider the online $0-1$ covering problems with non-convex objective. Building upon the resilient ideas from the primal-dual framework with configuration LPs, we derive a competitive algorithm for these problems. Our result generalizes the online primal-dual algorithm developed recently by Azar et al. for convex objectives with monotone gradients to non-convex objectives. The competitive ratio is now characterized by a new concept, called local smoothness --- a notion inspired by the smoothness. Our algorithm yields tight competitive ratio for the objectives such as the sum of $\ell_{k}$-norms and gives competitive solutions for online problems of submodular minimization and some natural non-convex minimization under covering constraints.

Includes members:
  Member "online covering with sum of ell_q norm objectives":
    also connected through other hyperedges:
      (none)
  Member "online submodular maximization with preemption":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale")
  Member "online contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "welfare maximization with production costs a primal dual approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reducing inefficiency in carbon auctions with imperfect competition")
  Member "tight bounds for online vector scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "streaming algorithms for bin packing and vector scheduling")
  Member "selfishmigrate a scalable algorithm for non clairvoyantly scheduling heterogeneous processors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a unifying optimal control framework for online job scheduling with general cost functions")
  Member "optimal approximation for submodular and supermodular optimization with bounded curvature":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularized submodular maximization at scale")
  Member "online primal dual for non linear optimization with applications to speed scaling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "station assignment with reallocation")

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)