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: "bandit learning in concave n person games"
  Abstract: This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games. The bandit framework accounts for extremely low-information environments where the agents may not even know they are playing a game; as such, the agents' most sensible choice in this setting would be to employ a no-regret learning algorithm. In general, this does not mean that the players' behavior stabilizes in the long run: no-regret learning may lead to cycles, even with perfect gradient information. However, if a standard monotonicity condition is satisfied, our analysis shows that no-regret learning based on mirror descent with bandit feedback converges to Nash equilibrium with probability $1$. We also derive an upper bound for the convergence rate of the process that nearly matches the best attainable rate for single-agent bandit stochastic optimization.

Connected through hyperedge HE-1 (induced by source paper "on gradient based learning in continuous games"):
  with members:
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "a generic approach for escaping saddle points"
    - "cycles in adversarial regularized learning"
    - "improved training of wasserstein gans"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-2 (induced by source paper "distributed no regret learning in multi agent systems"):
  with members:
    - "policy regret in repeated games"
    - "negative momentum for improved game dynamics"
    - "learning in time varying games"
    - "cycles in adversarial regularized learning"
    - "training gans with optimism"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "delay and cooperation in nonstochastic bandits"
    - "learning and efficiency in games with dynamic population"

Connected through hyperedge HE-3 (induced by source paper "no regret learning in cournot games"):
  with members:
    - "introduction to online convex optimization"
    - "finding friend and foe in multi agent games"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "learning in games robustness of fast convergence"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "fast convergence of regularized learning in games"
    - "on the role of a market maker in networked cournot competition"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (induced by source paper "learning probably approximately correct maximin strategies in simulation based games with infinite strategy spaces"):
  with members:
    - "competitive bridge bidding with deep neural networks"
    - "multiagent evaluation under incomplete information"
    - "large scale learning of agent rationality in two player zero sum games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "a generalised method for empirical game theoretic analysis"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "safe and nested subgame solving for imperfect information games"
    - "on kernelized multi armed bandits"

Connected through hyperedge HE-5 (induced by source paper "learning in time varying games"):
  with members:
    - "cycles in adversarial regularized learning"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "distributed stochastic optimization via matrix exponential learning"
    - "online optimization competing with dynamic comparators"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)