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: "on the limitations of first order approximation in gan dynamics"
  Abstract: While Generative Adversarial Networks (GANs) have demonstrated promising performance on multiple vision tasks, their learning dynamics are not yet well understood, both in theory and in practice. To address this issue, we study GAN dynamics in a simple yet rich parametric model that exhibits several of the common problematic convergence behaviors such as vanishing gradients, mode collapse, and diverging or oscillatory behavior. In spite of the non-convex nature of our model, we are able to perform a rigorous theoretical analysis of its convergence behavior. Our analysis reveals an interesting dichotomy: a GAN with an optimal discriminator provably converges, while first order approximations of the discriminator steps lead to unstable GAN dynamics and mode collapse. Our result suggests that using first order discriminator steps (the de-facto standard in most existing GAN setups) might be one of the factors that makes GAN training challenging in practice.

Connected through hyperedge HE-1 (induced by source paper "smoothness and stability in gans"):
  with members:
    - "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"
    - "beyond local nash equilibria for adversarial networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation through self supervision"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "domain generalization by solving jigsaw puzzles"
    - "boosting supervision with self supervision for few shot learning"
    - "stabilizing gan training with multiple random projections"
    - "deep visual domain adaptation a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-3 (induced by source paper "a closer look at the optimization landscapes of generative adversarial networks"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "local saddle point optimization a curvature exploitation approach"
    - "a variational inequality perspective on generative adversarial nets"
    - "on the convergence of gradient based learning in continuous games"
    - "the mechanics of n player differentiable games"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-4 (induced by source paper "encoding invariances in deep generative models"):
  with members:
    - "enforcing constraints for interpolation and extrapolation in generative adversarial networks"
    - "spatially constrained generative adversarial networks for conditional image generation"
    - "constrained image generation using binarized neural networks with decision procedures"
    - "large scale gan training for high fidelity natural image synthesis"
    - "do gan loss functions really matter"
    - "on convergence and stability of gans"
    - "bias and generalization in deep generative models an empirical study"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-5 (induced by source paper "towards a theoretical understanding of hashing based neural nets"):
  with members:
    - "on the convergence rate of training recurrent neural networks"
    - "adversarial examples from cryptographic pseudo random generators"
    - "spectral signatures in backdoor attacks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "the computational complexity of training relu s"
    - "adversarially robust generalization requires more data"
    - "a convergence theory for deep learning via over parameterization"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-6 (induced by source paper "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"):
  with members:
    - "continual state representation learning for reinforcement learning using generative replay"
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "experience replay for continual learning"
    - "two geometric input transformation methods for fast online reinforcement learning with neural nets"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "deep generative dual memory network for continual learning"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "selective experience replay for lifelong learning"

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)