human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "optimistic regret minimization for extensive form games via dilated distance generating functions"):
  with members:
    - "introduction to online convex optimization"
    - "stable predictive optimistic counterfactual regret minimization"
    - "solving imperfect information games via discounted regret minimization"
    - "solving large sequential games with the excessive gap technique"
    - "depth limited solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "imperfect recall abstractions with bounds in games"
    - "fast convergence of regularized learning in games"

Question: Please tell me which class the center node 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)