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 sample analyses for td 0 with function approximation"
  Abstract: TD(0) is one of the most commonly used algorithms in reinforcement learning. Despite this, there is no existing finite sample analysis for TD(0) with function approximation, even for the linear case. Our work is the first to provide such results. Existing convergence rates for Temporal Difference (TD) methods apply only to somewhat modified versions, e.g., projected variants or ones where stepsizes depend on unknown problem parameters. Our analyses obviate these artificial alterations by exploiting strong properties of TD(0). We provide convergence rates both in expectation and with high-probability. The two are obtained via different approaches that use relatively unknown, recently developed stochastic approximation techniques.

Connected through hyperedge HE-1 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "reanalysis of variance reduced temporal difference learning"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "concentration bounds for temporal difference learning with linear function approximation the case of batch data and uniform sampling"):
  with members:
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "on convergence of emphatic temporal difference learning"
    - "fast lstd using stochastic approximation finite time analysis and application to traffic control"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (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"
    - "fastest convergence for q learning"

Connected through hyperedge HE-5 (induced by source paper "optimization for reinforcement learning from single agent to cooperative agents"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "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"

Connected through hyperedge HE-6 (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"
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "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"

Connected through hyperedge HE-7 (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"
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "target based temporal difference learning"):
  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"
    - "sbeed convergent reinforcement learning with nonlinear 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"

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.AI(Artificial Intelligence)