human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "expected sarsa λ with control variate for variance reduction"):
  Abstract: Off-policy learning is powerful for reinforcement learning. However, the high variance of off-policy evaluation is a critical challenge, which causes off-policy learning with function approximation falls into an uncontrolled instability. In this paper, for reducing the variance, we introduce control variate technique to Expected Sarsa($\lambda$) and propose a tabular ES($\lambda$)-CV algorithm. We prove that if a proper estimator of value function reaches, the proposed ES($\lambda$)-CV enjoys a lower variance than Expected Sarsa($\lambda$). Furthermore, to extend ES($\lambda$)-CV to be a convergent algorithm with linear function approximation, we propose the GES($\lambda$) algorithm under the convex-concave saddle-point formulation. We prove that the convergence rate of GES($\lambda$) achieves $\mathcal{O}(1/T)$, which matches or outperforms several state-of-art gradient-based algorithms, but we use a more relaxed step-size. Numerical experiments show that the proposed algorithm is stable and converges faster with lower variance than several state-of-art gradient-based TD learning algorithms: GQ($\lambda$), GTB($\lambda$) and ABQ($\zeta$).

Includes members:
  Member "per decision multi step temporal difference learning with control variates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the concept of criticality in reinforcement learning")
  Member "finite sample analysis of the gtd policy evaluation algorithms in markov setting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reanalysis of variance reduced temporal difference learning")
  Member "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reanalysis of variance reduced temporal difference learning")
  Member "multi step off policy learning without importance sampling ratios":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive trade offs in off policy learning")
  Member "finite sample analyses for td 0 with function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")
  Member "convergent tree backup and retrace with function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ranking policy gradient")
  Member "investigating practical linear temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "temporal difference learning with neural networks study of the leakage propagation problem")
  Member "an emphatic approach to the problem of off policy temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction")

Question: Please tell me which class the source paper 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)