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: "finite time error bounds for linear stochastic approximation and td learning"
  Abstract: We consider the dynamics of a linear stochastic approximation algorithm driven by Markovian noise, and derive finite-time bounds on the moments of the error, i.e., deviation of the output of the algorithm from the equilibrium point of an associated ordinary differential equation (ODE). We obtain finite-time bounds on the mean-square error in the case of constant step-size algorithms by considering the drift of an appropriately chosen Lyapunov function. The Lyapunov function can be interpreted either in terms of Stein's method to obtain bounds on steady-state performance or in terms of Lyapunov stability theory for linear ODEs. We also provide a comprehensive treatment of the moments of the square of the 2-norm of the approximation error. Our analysis yields the following results: (i) for a given step-size, we show that the lower-order moments can be made small as a function of the step-size and can be upper-bounded by the moments of a Gaussian random variable; (ii) we show that the higher-order moments beyond a threshold may be infinite in steady-state; and (iii) we characterize the number of samples needed for the finite-time bounds to be of the same order as the steady-state bounds. As a by-product of our analysis, we also solve the open problem of obtaining finite-time bounds for the performance of temporal difference learning algorithms with linear function approximation and a constant step-size, without requiring a projection step or an i.i.d. noise assumption.

Connected through hyperedge HE-1 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling"):
  with members:
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "two time scale off policy td learning non asymptotic analysis over markovian samples"
    - "on the convergence proof of amsgrad and a new version"
    - "smoothing policies and safe policy gradients"
    - "finite sample analysis for sarsa with linear function approximation"
    - "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory"
    - "neural temporal difference learning converges to global optima"
    - "global optimality guarantees for policy gradient methods"

Connected through hyperedge HE-2 (induced by source paper "reanalysis of variance reduced temporal difference learning"):
  with members:
    - "two time scale off policy td learning non asymptotic analysis over markovian samples"
    - "finite sample analysis for sarsa with linear function approximation"
    - "svrg for policy evaluation with fewer gradient evaluations"
    - "target based temporal difference learning"
    - "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory"
    - "neural temporal difference learning converges to global optima"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "finite sample analysis of the gtd policy evaluation algorithms in markov setting"

Connected through hyperedge HE-3 (induced by source paper "finite sample analysis of stochastic approximation using smooth convex envelopes"):
  with members:
    - "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"
    - "fastest convergence for q learning"

Connected through hyperedge HE-4 (induced by source paper "optimization for reinforcement learning from single agent to cooperative agents"):
  with members:
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "addressing function approximation error in actor critic methods"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"

Connected through hyperedge HE-5 (induced by source paper "neural temporal difference learning converges to global optima"):
  with members:
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "td or not td analyzing the role of temporal differencing in deep reinforcement learning"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-6 (induced by source paper "a finite time analysis of q learning with neural network function approximation"):
  with members:
    - "a gram gauss newton method learning overparameterized deep neural networks for regression problems"
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "a theoretical analysis of deep q learning"
    - "an improved analysis of training over parameterized deep neural networks"
    - "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory"
    - "neural temporal difference learning converges to global optima"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "target based temporal difference learning"):
  with members:
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "finite sample analyses for td 0 with function approximation"
    - "learning from conditional distributions via dual embeddings"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances"):
  with members:
    - "a communication efficient multi agent actor critic algorithm for distributed reinforcement learning"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "on convergence of emphatic temporal difference learning"
    - "distributed policy evaluation under multiple behavior strategies"

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)