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: "stable opponent shaping in differentiable games"
  Abstract: A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity to multi-agent RL. Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on others' updates. Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm that exploits this response and leads to cooperation in settings like the Iterated Prisoner's Dilemma. Although experimentally successful, we show that LOLA agents can exhibit 'arrogant' behaviour directly at odds with convergence. In fact, remarkably few algorithms have theoretical guarantees applying across all (n-player, non-convex) games. In this paper we present Stable Opponent Shaping (SOS), a new method that interpolates between LOLA and a stable variant named LookAhead. We prove that LookAhead converges locally to equilibria and avoids strict saddles in all differentiable games. SOS inherits these essential guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.

Connected through hyperedge HE-1 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization"):
  with members:
    - "from darwin to poincar e and von neumann recurrence and cycles in evolutionary and algorithmic game theory"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "a theory of regularized markov decision processes"
    - "openspiel a framework for reinforcement learning in games"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the mechanics of n player differentiable games"
    - "modeling friends and foes"
    - "negative momentum for improved game dynamics"

Connected through hyperedge HE-2 (induced by source paper "optimizing millions of hyperparameters by implicit differentiation"):
  with members:
    - "dataset distillation"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "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"

Connected through hyperedge HE-3 (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"
    - "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"

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.MA(Multiagent Systems)