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: "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"
  Abstract: We relate the minimax game of generative adversarial networks (GANs) to finding the saddle points of the Lagrangian function for a convex optimization problem, where the discriminator outputs and the distribution of generator outputs play the roles of primal variables and dual variables, respectively. This formulation shows the connection between the standard GAN training process and the primal-dual subgradient methods for convex optimization. The inherent connection does not only provide a theoretical convergence proof for training GANs in the function space, but also inspires a novel objective function for training. The modified objective function forces the distribution of generator outputs to be updated along the direction according to the primal-dual subgradient methods. A toy example shows that the proposed method is able to resolve mode collapse, which in this case cannot be avoided by the standard GAN or Wasserstein GAN. Experiments on both Gaussian mixture synthetic data and real-world image datasets demonstrate the performance of the proposed method on generating diverse samples.

Connected through hyperedge HE-1 (induced by source paper "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"):
  with members:
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "finite sample analysis of the gtd policy evaluation algorithms in markov setting"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-2 (induced by source paper "generative adversarial networks for mitigating biases in machine learning systems"):
  with members:
    - "improved adversarial learning for fair classification"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "learning non discriminatory predictors"
    - "improved training of wasserstein gans"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "fictitious gan training gans with historical models"):
  with members:
    - "beyond local nash equilibria for adversarial networks"
    - "dual discriminator generative adversarial nets"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "an online learning approach to generative adversarial networks"
    - "multi view generative adversarial networks"
    - "multi generator generative adversarial nets"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-4 (induced by source paper "a convex duality framework for gans"):
  with members:
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "solving approximate wasserstein gans to stationarity"
    - "a classification based study of covariate shift in gan distributions"
    - "the numerics of gans"
    - "approximation and convergence properties of generative adversarial learning"
    - "stabilizing training of generative adversarial networks through regularization"
    - "f gans in an information geometric nutshell"
    - "improved training of wasserstein gans"

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)