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: "optimality and stability in non convex non concave min max optimization"
  Abstract: Convergence to a saddle point for convex-concave functions has been studied for decades, while the last few years have seen a surge of interest in non-convex-non-concave min-max optimization due to the rise of deep learning. However, it remains an intriguing research challenge how local optimal points are defined and which algorithm can converge to such points. We study definitions of "local min-max (max-min)" points and provide an elegant unification, with the corresponding first- and second-order necessary and sufficient conditions. Specifically, we show that quadratic games, as often used as illustrative examples and approximations of smooth functions, are too special, both locally and globally. Lastly, we analyze the exact conditions for local convergence of several popular gradient algorithms near the "local min-max" points defined in the previous section, identify "valid" hyper-parameters and compare the respective stable sets. Our results offer insights into the necessity of two-time-scale algorithms and the limitation of the commonly used approach based on ordinary differential equations.

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)