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 convergence of gradient based learning in continuous games"
  Abstract: We study the limiting behavior of competitive agents employing gradient-based learning algorithms through the lens of dynamical systems theory. Specifically, we introduce a general framework for competitive gradient-based learning that allows us to analyze a wide breadth of learning algorithms including policy gradient reinforcement learning, gradient based bandits, and certain online convex optimization algorithms. We show that for both potential games and general-sum games, when agents employ gradient-based learning algorithms, they will avoid a non-negligible subset of the local Nash equilibria. This is a strongly negative result for gradient-based learning in games. Our framework also sheds light on the issue of convergence to non-Nash strategies in general-sum and zero-sum games which have no relevance to the underlying game, and arise solely due to the choice of algorithm. The existence and frequency of strategies may explain some of the difficulties encountered when using gradient descent in zero-sum games (e.g. to train generative adversarial networks). Finally, we introduce a new class of games, Morse-Smale games, for which the gradient dynamics correspond to gradient-like flows. This class encompasses a large set of commonly encountered games. For Morse-Smale games, we show that competitive gradient-based learning converges to either limit cycles, Nash equilibria, or non-Nash fixed points almost surely. To reinforce our theoretical contributions, we provide empirical results that highlight the frequency of Nash equilibria that are almost surely avoided by policy gradient in linear quadratic games. Indeed, we present empirical results that show that policy gradient almost surely avoids the unique global Nash equilibrium in one out of five randomly sampled linear quadratic games.

Connected through hyperedge HE-1 (induced by source paper "inference based strategy alignment for general sum differential games"):
  with members:
    - "on gradient based learning in continuous games"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "multimodal probabilistic model based planning for human robot interaction"

Connected through hyperedge HE-2 (induced by source paper "implicit competitive regularization in gans"):
  with members:
    - "coherent gradients an approach to understanding generalization in gradient descent based optimization"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a gradual semi discrete approach to generative network training via explicit wasserstein minimization"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "implicit regularization in deep matrix factorization"
    - "implicit regularization in over parameterized neural networks"

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"
    - "the mechanics of n player differentiable games"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "negative momentum for improved game dynamics"

Connected through hyperedge HE-4 (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 gradient descent ascent for nonconvex concave minimax problems"
    - "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"
    - "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-5 (induced by source paper "what is local optimality in nonconvex nonconcave minimax optimization"):
  with members:
    - "on gradient based learning in continuous games"
    - "introduction to online convex optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "local saddle point optimization a curvature exploitation approach"
    - "learning in non convex games with an optimization oracle"
    - "negative momentum for improved game dynamics"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "policy gradient algorithms have no guarantees of convergence in continuous action and state multi agent settings"):
  with members:
    - "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"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "emergent complexity via multi agent competition"
    - "training gans with optimism"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-7 (induced by source paper "policy gradient algorithms have no guarantees of convergence in linear quadratic games"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "emergent complexity via multi agent competition"
    - "cycles in adversarial regularized learning"
    - "a unified game theoretic approach to multiagent reinforcement 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)