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 td 0 with function approximation concentration bounds and a centered variant with exponential convergence"
  Abstract: We provide non-asymptotic bounds for the well-known temporal difference learning algorithm TD(0) with linear function approximators. These include high-probability bounds as well as bounds in expectation. Our analysis suggests that a step-size inversely proportional to the number of iterations cannot guarantee optimal rate of convergence unless we assume (partial) knowledge of the stationary distribution for the Markov chain underlying the policy considered. We also provide bounds for the iterate averaged TD(0) variant, which gets rid of the step-size dependency while exhibiting the optimal rate of convergence. Furthermore, we propose a variant of TD(0) with linear approximators that incorporates a centering sequence, and establish that it exhibits an exponential rate of convergence in expectation. We demonstrate the usefulness of our bounds on two synthetic experimental settings.

Connected through hyperedge HE-1 (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-2 (induced by source paper "expected sarsa λ with control variate for variance reduction"):
  with members:
    - "per decision multi step temporal difference learning with control variates"
    - "finite sample analysis of the gtd policy evaluation algorithms in markov setting"
    - "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"
    - "multi step off policy learning without importance sampling ratios"
    - "finite sample analyses for td 0 with function approximation"
    - "convergent tree backup and retrace with function approximation"
    - "investigating practical linear temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"

Connected through hyperedge HE-3 (induced by source paper "svrg for policy evaluation with fewer gradient evaluations"):
  with members:
    - "stochastic variance reduced policy gradient"
    - "stochastic variance reduction methods for policy evaluation"
    - "convergent tree backup and retrace with function approximation"
    - "stochastic variance reduction methods for saddle point problems"
    - "stop wasting my gradients practical svrg"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-4 (induced by source paper "a finite time analysis of temporal difference learning with linear function approximation"):
  with members:
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

Connected through hyperedge HE-5 (induced by source paper "stochastic variance reduction methods for policy evaluation"):
  with members:
    - "learning from conditional distributions via dual embeddings"
    - "stochastic variance reduction methods for saddle point problems"
    - "learning from conditional distributions via dual kernel embeddings"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "distributed policy evaluation under multiple behavior strategies"

Connected through hyperedge HE-6 (induced by source paper "finite sample analyses for td 0 with function approximation"):
  with members:
    - "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"
    - "accelerated gradient temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"

Connected through hyperedge HE-7 (induced by source paper "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"):
  with members:
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "constant step size least mean square bias variance trade offs and optimal sampling distributions"

Connected through hyperedge HE-8 (induced by source paper "variance constrained actor critic algorithms for discounted and average reward mdps"):
  with members:
    - "policy evaluation with variance related risk criteria in markov decision processes"
    - "policy gradients with variance related risk criteria"
    - "mean variance optimization in markov decision processes"
    - "infinite horizon policy gradient estimation"

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)