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 "more robust doubly robust off policy evaluation"):
  Abstract: We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of a policy from the data generated by another policy(ies). In particular, we focus on the doubly robust (DR) estimators that consist of an importance sampling (IS) component and a performance model, and utilize the low (or zero) bias of IS and low variance of the model at the same time. Although the accuracy of the model has a huge impact on the overall performance of DR, most of the work on using the DR estimators in OPE has been focused on improving the IS part, and not much on how to learn the model. In this paper, we propose alternative DR estimators, called more robust doubly robust (MRDR), that learn the model parameter by minimizing the variance of the DR estimator. We first present a formulation for learning the DR model in RL. We then derive formulas for the variance of the DR estimator in both contextual bandits and RL, such that their gradients w.r.t.~the model parameters can be estimated from the samples, and propose methods to efficiently minimize the variance. We prove that the MRDR estimators are strongly consistent and asymptotically optimal. Finally, we evaluate MRDR in bandits and RL benchmark problems, and compare its performance with the existing methods.

Includes members:
  Member "the reactor a fast and sample efficient actor critic agent for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "high confidence off policy evaluation with models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding the artificial intelligence clinician and optimal treatment strategies for sepsis in intensive care")
  Member "off policy evaluation for slate recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "delay adaptive learning in generalized linear contextual bandits")
  Member "doubly robust off policy value evaluation for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  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 "unbiased offline evaluation of contextual bandit based news article recommendation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised contextual bandits in computer vision")
  Member "learning from scarce experience":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a nonparametric off policy policy gradient")

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.AI(Artificial Intelligence)