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 gradient descent ascent for nonconvex concave minimax problems"
  Abstract: We consider nonconvex-concave minimax problems, $\min_{x} \max_{y\in\mathcal{Y}} f(x, y)$, where $f$ is nonconvex in $x$ but concave in $y$. The standard algorithm for solving this problem is the celebrated gradient descent ascent (GDA) algorithm, which has been widely used in machine learning, control theory and economics. However, despite the solid theory for the convex-concave setting, GDA can converge to limit cycles or even diverge in a general setting. In this paper, we present a nonasymptotic analysis of GDA for solving nonconvex-concave minimax problems, showing that GDA can find a stationary point of the function $\Phi(\cdot) :=\max_{y\in\mathcal{Y} }f(\cdot, y)$ efficiently. To the best our knowledge, this is the first theoretical guarantee for GDA in this setting, shedding light on its practical performance in many real applications.

Connected through hyperedge HE-1 (induced by source paper "gans may have no nash equilibria"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "lipschitz generative adversarial nets"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "sgd learns one layer networks in wgans"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "convergence of learning dynamics in stackelberg games"
    - "a convex duality framework for gans"

Connected through hyperedge HE-2 (induced by source paper "when can wasserstein gans minimize wasserstein distance"):
  with members:
    - "sgd learns one layer networks in wgans"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "approximability of discriminators implies diversity in gans"
    - "spectral normalization for generative adversarial networks"
    - "negative momentum for improved game dynamics"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "the numerics of gans"

Connected through hyperedge HE-3 (induced by source paper "a fast saddle point dynamical system approach to robust deep learning"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "scaling provable adversarial defenses"
    - "trust region based adversarial attack on neural networks"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "robust optimization for non convex objectives"
    - "practical black box attacks against machine learning"
    - "measuring neural net robustness with constraints"

Connected through hyperedge HE-4 (induced by source paper "stochastic recursive gradient descent ascent for stochastic nonconvex strongly concave minimax problems"):
  with members:
    - "a stochastic proximal point algorithm for saddle point problems"
    - "stochastic variance reduction methods for policy evaluation"
    - "stochastic variance reduction methods for saddle point problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-5 (induced by source paper "on solving minimax optimization locally a follow the ridge approach"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "convergence of learning dynamics in stackelberg games"
    - "local saddle point optimization a curvature exploitation approach"
    - "the mechanics of n player differentiable games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "global convergence to the equilibrium of gans using variational inequalities"

Connected through hyperedge HE-6 (induced by source paper "convergence of learning dynamics in stackelberg games"):
  with members:
    - "on gradient based learning in continuous games"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "on the convergence of gradient based learning in continuous games"
    - "the mechanics of n player differentiable games"
    - "goal inference improves objective and perceived performance in human robot collaboration"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"

Connected through hyperedge HE-7 (induced by source paper "sgd learns one layer networks in wgans"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "learning distributions generated by one layer relu networks"
    - "approximability of discriminators implies diversity in gans"
    - "negative momentum for improved game dynamics"
    - "which training methods for gans do actually converge"
    - "learning one hidden layer neural networks with landscape design"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "the numerics of 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)