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 "smooth markets a basic mechanism for organizing gradient based learners"):
  Abstract: With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games. We therefore introduce smooth markets (SM-games), a class of n-player games with pairwise zero sum interactions. SM-games codify a common design pattern in machine learning that includes (some) GANs, adversarial training, and other recent algorithms. We show that SM-games are amenable to analysis and optimization using first-order methods.

Includes members:
  Member "logan latent optimisation for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "your gan is secretly an energy based model and you should use discriminator driven latent sampling")
  Member "finite regret and cycles with fixed step size via alternating gradient descent ascent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "last iterate is slower than averaged iterate in smooth convex concave saddle point problems")
  Member "a closer look at the optimization landscapes of generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a unified analysis of gradient based methods for a whole spectrum of games")
  Member "differentiable game mechanics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accelerating smooth games by manipulating spectral shapes")
  Member "the mechanics of n player differentiable games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization")
  Member "global convergence to the equilibrium of gans using variational inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit competitive regularization in gans")
  Member "the numerics of gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "last iterate convergence in no regret learning constrained min max optimization for convex concave landscapes")

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)