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 "last iterate is slower than averaged iterate in smooth convex concave saddle point problems"):
  Abstract: In this paper we study the smooth convex-concave saddle point problem. Specifically, we analyze the last iterate convergence properties of the Extragradient (EG) algorithm. It is well known that the ergodic (averaged) iterates of EG converge at a rate of $O(1/T)$ (Nemirovski, 2004). In this paper, we show that the last iterate of EG converges at a rate of $O(1/\sqrt{T})$. To the best of our knowledge, this is the first paper to provide a convergence rate guarantee for the last iterate of EG for the smooth convex-concave saddle point problem. Moreover, we show that this rate is tight by proving a lower bound of $\Omega(1/\sqrt{T})$ for the last iterate. This lower bound therefore shows a quadratic separation of the convergence rates of ergodic and last iterates in smooth convex-concave saddle point problems.

Includes members:
  Member "accelerating smooth games by manipulating spectral shapes":
    also connected through other hyperedges:
      (none)
  Member "a tight and unified analysis of extragradient for a whole spectrum of differentiable games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accelerating smooth games by manipulating spectral shapes")
  Member "training gans with centripetal acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimality and stability in non convex non concave min max optimization")
  Member "lower bounds and conditioning of differentiable games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimality and stability in non convex non concave min max optimization")
  Member "finite regret and cycles with fixed step size via alternating gradient descent ascent":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners")
  Member "a variational inequality perspective on generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local nash equilibria are isolated strict local nash equilibria in almost all zero sum continuous games")
  Member "optimistic mirror descent in saddle point problems going the extra gradient mile":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimality and stability in non convex non concave min max optimization")
  Member "negative momentum for improved game dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization")

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)