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: "fastest convergence for q learning"
  Abstract: The Zap Q-learning algorithm introduced in this paper is an improvement of Watkins' original algorithm and recent competitors in several respects. It is a matrix-gain algorithm designed so that its asymptotic variance is optimal. Moreover, an ODE analysis suggests that the transient behavior is a close match to a deterministic Newton-Raphson implementation. This is made possible by a two time-scale update equation for the matrix gain sequence. #R##N#The analysis suggests that the approach will lead to stable and efficient computation even for non-ideal parameterized settings. Numerical experiments confirm the quick convergence, even in such non-ideal cases. #R##N#A secondary goal of this paper is tutorial. The first half of the paper contains a survey on reinforcement learning algorithms, with a focus on minimum variance algorithms.

Connected through hyperedge HE-1 (induced by source paper "finite sample analysis of stochastic approximation using smooth convex envelopes"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "finite sample analyses for td 0 with function approximation"

Connected through hyperedge HE-2 (induced by source paper "accelerated target updates for q learning"):
  with members:
    - "deepmind control suite"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "reinforcement learning meets hybrid zero dynamics a case study for rabbit"
    - "proximal policy optimization algorithms"
    - "convergence analysis of two layer neural networks with relu activation"
    - "prioritized experience replay"
    - "action conditional video prediction using deep networks in atari games"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "zap q learning with nonlinear function approximation"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "tracking control by the newton raphson flow applications to autonomous vehicles"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "concentration bounds for two timescale stochastic approximation with applications to reinforcement learning"
    - "stability of stochastic approximations with controlled markov noise and temporal difference learning"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"

Connected through hyperedge HE-4 (induced by source paper "differential temporal difference learning"):
  with members:
    - "differential td learning for value function approximation"
    - "approximate dynamic programming using fluid and diffusion approximations with applications to power management"

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.SY(Systems and Control)