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 "first order methods with increasing iterate averaging for solving saddle point problems"):
  Abstract: First-order methods are known to be among the fastest algorithms for solving large-scale convex-concave saddle-point problems. Algorithms that achieve a theoretical convergence rate on the order of $1/T$ are known, but these often rely on uniformly averaging iterates in order to get the guaranteed rate. In contrast, using the last iterate has repeatedly been found to perform better in practice, but with no guarantee on convergence rate. In this paper we propose using averaging schemes with increasing weight on recent iterates, which leads to a guaranteed $1/T$ convergence rate, while capturing the practical performance of using the last iterate. We show this for Chambolle and Pock's primal-dual algorithm, and mirror prox. We present numerical results on computing Nash equilibria in matrix games, competitive equilibria in Fisher markets, and image denoising via total-variation minimization under the $\ell_1$ norm. In all cases we find that our averaging schemes lead to much better performance than uniform averaging, and sometimes even better performance than using the last iterate.

Includes members:
  Member "computing large market equilibria using abstractions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "increasing iterate averaging for solving saddle point problems")
  Member "solving large sequential games with the excessive gap technique":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "increasing iterate averaging for solving saddle point problems")
  Member "online convex optimization for sequential decision processes and extensive form games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic regret minimization in extensive form games")
  Member "pacing equilibrium in first price auction markets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stability and learning in strategic queuing systems")
  Member "multiplicative pacing equilibria in auction markets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stability and learning in strategic queuing systems")

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.LG(Machine Learning)