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: "actor critic algorithms for learning nash equilibria in n player general sum games"
  Abstract: We consider the problem of finding stationary Nash equilibria (NE) in a finite discounted general-sum stochastic game. We first generalize a non-linear optimization problem from Filar and Vrieze [2004] to a $N$-player setting and break down this problem into simpler sub-problems that ensure there is no Bellman error for a given state and an agent. We then provide a characterization of solution points of these sub-problems that correspond to Nash equilibria of the underlying game and for this purpose, we derive a set of necessary and sufficient SG-SP (Stochastic Game - Sub-Problem) conditions. Using these conditions, we develop two actor-critic algorithms: OFF-SGSP (model-based) and ON-SGSP (model-free). Both algorithms use a critic that estimates the value function for a fixed policy and an actor that performs descent in the policy space using a descent direction that avoids local minima. We establish that both algorithms converge, in self-play, to the equilibria of a certain ordinary differential equation (ODE), whose stable limit points coincide with stationary NE of the underlying general-sum stochastic game. On a single state non-generic game (see Hart and Mas-Colell [2005]) as well as on a synthetic two-player game setup with $810,000$ states, we establish that ON-SGSP consistently outperforms NashQ ([Hu and Wellman, 2003] and FFQ [Littman, 2001] algorithms.

Connected through hyperedge HE-1 (induced by source paper "a convergence result for regularized actor critic methods"):
  with members:
    - "a theory of regularized markov decision processes"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "equivalence between policy gradients and soft q learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "proximal policy optimization algorithms"
    - "pgq combining policy gradient and q learning"
    - "taming the noise in reinforcement learning via soft updates"

Connected through hyperedge HE-2 (induced by source paper "stochastic convergence results for regularized actor critic methods"):
  with members:
    - "a theory of regularized markov decision processes"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "equivalence between policy gradients and soft q learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "proximal policy optimization algorithms"
    - "pgq combining policy gradient and q learning"
    - "dynamic policy programming"

Connected through hyperedge HE-3 (induced by source paper "fully decentralized multi agent reinforcement learning with networked agents"):
  with members:
    - "diff dac distributed actor critic for multitask deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "deep reinforcement learning an overview"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
    - "distral robust multitask 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.GT(Computer Science and Game Theory)