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: "balancing two player stochastic games with soft q learning"
  Abstract: Within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations, where humans face tough adversarial decision makers. Current frameworks for stochastic games and reinforcement learning prohibit tuneable strategies as they seek optimal performance. In this paper, we enable such tuneable behaviour by generalising soft Q-learning to stochastic games, where more than one agent interact strategically. We contribute both theoretically and empirically. On the theory side, we show that games with soft Q-learning exhibit a unique value and generalise team games and zero-sum games far beyond these two extremes to cover a continuous spectrum of gaming behaviour. Experimentally, we show how tuning agents' constraints affect performance and demonstrate, through a neural network architecture, how to reliably balance games with high-dimensional representations.

Connected through hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "emergent tool use from multi agent autocurricula"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "sample optimal parametric q learning using linearly additive features"
    - "frequentist regret bounds for randomized least squares value iteration"

Connected through hyperedge HE-2 (induced by source paper "a regularized opponent model with maximum entropy objective"):
  with members:
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "learning to communicate implicitly by actions"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "machine theory of mind"

Connected through hyperedge HE-3 (induced by source paper "alpha alpha rank practically scaling alpha rank through stochastic optimisation"):
  with members:
    - "a generalized training approach for multiagent learning"
    - "multiagent evaluation under incomplete information"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "multi agent generalized recursive reasoning"
    - "openspiel a framework for reinforcement learning in games"
    - "bi level actor critic for multi agent coordination"
    - "open ended learning in symmetric zero sum games"

Connected through hyperedge HE-4 (induced by source paper "improving fictitious play reinforcement learning with expanding models"):
  with members:
    - "neural fictitious self play on elf mini rts"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "openspiel a framework for reinforcement learning in games"
    - "open ended learning in symmetric zero sum games"
    - "deep counterfactual regret minimization"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-5 (induced by source paper "coordinating the crowd inducing desirable equilibria in non cooperative systems"):
  with members:
    - "decentralised learning in systems with many many strategic agents"
    - "learning parametric closed loop policies for markov potential games"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "dynamic potential games in communications fundamentals and applications"
    - "spectrum sharing as spatial congestion games"

Connected through hyperedge HE-6 (induced by source paper "regularised deep reinforcement learning with guaranteed convergence"):
  with members:
    - "decentralised learning in systems with many many strategic agents"
    - "an information theoretic on line update principle for perception action coupling"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization 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.AI(Artificial Intelligence)