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: "on the global convergence of imitation learning a case for linear quadratic regulator"
  Abstract: We study the global convergence of generative adversarial imitation learning for linear quadratic regulators, which is posed as minimax optimization. To address the challenges arising from non-convex-concave geometry, we analyze the alternating gradient algorithm and establish its Q-linear rate of convergence to a unique saddle point, which simultaneously recovers the globally optimal policy and reward function. We hope our results may serve as a small step towards understanding and taming the instability in imitation learning as well as in more general non-convex-concave alternating minimax optimization that arises from reinforcement learning and generative adversarial learning.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate"):
  with members:
    - "on computation and generalization of generative adversarial imitation learning"
    - "provably efficient reinforcement learning with linear function approximation"
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "sample optimal parametric q learning using linearly additive features"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-2 (induced by source paper "on computation and generalization of generative adversarial imitation learning"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "imitating driver behavior with generative adversarial networks"
    - "mmd gan towards deeper understanding of moment matching network"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-3 (induced by source paper "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"):
  with members:
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "finite sample analysis of the gtd policy evaluation algorithms in markov setting"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

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.LG(Machine Learning)