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: "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"
  Abstract: Ye showed recently that the simplex method with Dantzig pivoting rule, as well as Howard's policy iteration algorithm, solve discounted Markov decision processes (MDPs), with a constant discount factor, in strongly polynomial time. More precisely, Ye showed that both algorithms terminate after at most $O(\frac{mn}{1-\gamma}\log(\frac{n}{1-\gamma}))$ iterations, where $n$ is the number of states, $m$ is the total number of actions in the MDP, and $0<\gamma<1$ is the discount factor. We improve Ye's analysis in two respects. First, we improve the bound given by Ye and show that Howard's policy iteration algorithm actually terminates after at most $O(\frac{m}{1-\gamma}\log(\frac{n}{1-\gamma}))$ iterations. Second, and more importantly, we show that the same bound applies to the number of iterations performed by the strategy iteration (or strategy improvement) algorithm, a generalization of Howard's policy iteration algorithm used for solving 2-player turn-based stochastic games with discounted zero-sum rewards. This provides the first strongly polynomial algorithm for solving these games, resolving a long standing open problem.

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 "provable self play algorithms for competitive reinforcement learning"):
  with members:
    - "reward free exploration for reinforcement learning"
    - "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"
    - "online convex optimization in adversarial markov decision processes"
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "corruption robust exploration in episodic reinforcement learning"
    - "a review of cooperative multi agent deep reinforcement learning"
    - "feature based q learning for two player stochastic games"

Connected through hyperedge HE-3 (induced by source paper "on reinforcement learning for turn based zero sum markov games"):
  with members:
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "feature based q learning for two player stochastic games"
    - "harnessing structures for value based planning and reinforcement learning"
    - "q learning with nearest neighbors"
    - "online reinforcement learning in stochastic games"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "general linear group action on tensors a candidate for post quantum cryptography"):
  with members:
    - "isomorphism problems for tensors groups and cubic forms completeness and reductions"
    - "operator scaling via geodesically convex optimization invariant theory and polynomial identity testing"
    - "efficient algorithms for tensor scaling quantum marginals and moment polytopes"
    - "alternating minimization scaling algorithms and the null cone problem from invariant theory"
    - "algorithms based on algebras and their applications to isomorphism of polynomials with one secret group isomorphism and polynomial identity testing"
    - "constructive noncommutative rank computation is in deterministic polynomial time"
    - "solving the isomorphism of polynomials with two secrets problem for all pairs of quadratic forms"
    - "polynomial time algorithms for quadratic isomorphism of polynomials the regular case"

Connected through hyperedge HE-5 (induced by source paper "action robust reinforcement learning and applications in continuous control"):
  with members:
    - "adversarial attacks and defences competition"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating adversarial examples with adversarial networks"
    - "exploration conscious reinforcement learning revisited"
    - "parameter space noise for exploration"
    - "robust adversarial reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "feature based q learning for two player stochastic games"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "sample optimal parametric q learning with linear transition models"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "scalable bilinear pi learning using state and action features"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-7 (induced by source paper "solving discounted stochastic two player games with near optimal time and sample complexity"):
  with members:
    - "on the optimality of sparse model based planning for markov decision processes"
    - "online reinforcement learning in stochastic games"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "on the complexity of policy iteration"

Connected through hyperedge HE-8 (induced by source paper "apprenticeship learning via frank wolfe"):
  with members:
    - "average reward reinforcement learning with unknown mixing times"
    - "variance reduced and projection free stochastic optimization"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "projection free online 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)