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 "weak convergence properties of constrained emphatic temporal difference learning with constant and slowly diminishing stepsize"):
  Abstract: We consider the emphatic temporal-difference (TD) algorithm, ETD($\lambda$), for learning the value functions of stationary policies in a discounted, finite state and action Markov decision process. The ETD($\lambda$) algorithm was recently proposed by Sutton, Mahmood, and White to solve a long-standing divergence problem of the standard TD algorithm when it is applied to off-policy training, where data from an exploratory policy are used to evaluate other policies of interest. The almost sure convergence of ETD($\lambda$) has been proved in our recent work under general off-policy training conditions, but for a narrow range of diminishing stepsize. In this paper we present convergence results for constrained versions of ETD($\lambda$) with constant stepsize and with diminishing stepsize from a broad range. Our results characterize the asymptotic behavior of the trajectory of iterates produced by those algorithms, and are derived by combining key properties of ETD($\lambda$) with powerful convergence theorems from the weak convergence methods in stochastic approximation theory. For the case of constant stepsize, in addition to analyzing the behavior of the algorithms in the limit as the stepsize parameter approaches zero, we also analyze their behavior for a fixed stepsize and bound the deviations of their averaged iterates from the desired solution. These results are obtained by exploiting the weak Feller property of the Markov chains associated with the algorithms, and by using ergodic theorems for weak Feller Markov chains, in conjunction with the convergence results we get from the weak convergence methods. Besides ETD($\lambda$), our analysis also applies to the off-policy TD($\lambda$) algorithm, when the divergence issue is avoided by setting $\lambda$ sufficiently large.

Includes members:
  Member "some simulation results for emphatic temporal difference learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on generalized bellman equations and temporal difference learning")
  Member "emphatic temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stabilizing off policy q learning via bootstrapping error reduction")
  Member "an emphatic approach to the problem of off policy temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction")
  Member "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "off policy learning with eligibility traces a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algaedice policy gradient from arbitrary experience")
  Member "statistical linear estimation with penalized estimators an application to reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "information theoretic considerations in batch reinforcement learning")
  Member "sparse q learning with mirror descent":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "imitation projected programmatic reinforcement learning")
  Member "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reinforcement learning via fenchel rockafellar duality")

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)