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 solving minimax optimization locally a follow the ridge approach"
  Abstract: Many tasks in modern machine learning can be formulated as finding equilibria in \emph{sequential} games. In particular, two-player zero-sum sequential games, also known as minimax optimization, have received growing interest. It is tempting to apply gradient descent to solve minimax optimization given its popularity and success in supervised learning. However, it has been noted that naive application of gradient descent fails to find some local minimax and can converge to non-local-minimax points. In this paper, we propose \emph{Follow-the-Ridge} (FR), a novel algorithm that provably converges to and only converges to local minimax. We show theoretically that the algorithm addresses the notorious rotational behaviour of gradient dynamics, and is compatible with preconditioning and \emph{positive} momentum. Empirically, FR solves toy minimax problems and improves the convergence of GAN training compared to the recent minimax optimization algorithms.

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"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "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"
    - "convergence of learning dynamics in stackelberg games"
    - "a convex duality framework for gans"

Connected through hyperedge HE-2 (induced by source paper "optimality and stability in non convex non concave min max optimization"):
  with members:
    - "training gans with centripetal acceleration"
    - "convergence behaviour of some gradient based methods on bilinear games"
    - "lower bounds and conditioning of differentiable games"
    - "linear lower bounds and conditioning of differentiable games"
    - "convergence of learning dynamics in stackelberg games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "the numerics of gans"

Connected through hyperedge HE-3 (induced by source paper "optimizing millions of hyperparameters by implicit differentiation"):
  with members:
    - "dataset distillation"
    - "meta learning with implicit gradients"
    - "deep equilibrium models"
    - "autoassist a framework to accelerate training of deep neural networks"
    - "generalized inner loop meta learning"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "adversarial learning of general transformations for data augmentation"
    - "on first order meta learning algorithms"

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)