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: "beyond local nash equilibria for adversarial networks"
  Abstract: Save for some special cases, current training methods for Generative Adversarial Networks (GANs) are at best guaranteed to converge to a ‘local Nash equilibrium’ (LNE). Such LNEs, however, can be arbitrarily far from an actual Nash equilibrium (NE), which implies that there are no guarantees on the quality of the found generator or classifier. This paper proposes to model GANs explicitly as finite games in mixed strategies, thereby ensuring that every LNE is an NE. We use the Parallel Nash Memory as a solution method, which is proven to monotonically converge to a resource-bounded Nash equilibrium. We empirically demonstrate that our method is less prone to typical GAN problems such as mode collapse and produces solutions that are less exploitable than those produced by GANs and MGANs.

Connected through hyperedge HE-1 (induced by source paper "learning unbiased representations via mutual information backpropagation"):
  with members:
    - "on variational bounds of mutual information"
    - "flexibly fair representation learning by disentanglement"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "invariant representations without adversarial training"
    - "learning not to learn training deep neural networks with biased data"
    - "mitigating unwanted biases with adversarial learning"
    - "generalizing across domains via cross gradient training"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "smoothness and stability in gans"):
  with members:
    - "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "fictitious gan training gans with historical models"):
  with members:
    - "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"
    - "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 "finding mixed nash equilibria of generative adversarial networks"):
  with members:
    - "a variational inequality perspective on generative adversarial nets"
    - "pros and cons of gan evaluation measures"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "negative momentum for improved game dynamics"
    - "an online learning approach to generative adversarial networks"
    - "the numerics of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)