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 "optimal off policy evaluation for reinforcement learning with marginalized importance sampling"):
  Abstract: Motivated by the many real-world applications of reinforcement learning (RL) that require safe-policy iterations, we consider the problem of off-policy evaluation (OPE) --- the problem of evaluating a new policy using the historical data obtained by different behavior policies --- under the model of nonstationary episodic Markov Decision Processes with a long horizon and large action space. Existing importance sampling (IS) methods often suffer from large variance that depends exponentially on the RL horizon $H$. To solve this problem, we consider a marginalized importance sampling (MIS) estimator that recursively estimates the state marginal distribution for the target policy at every step. MIS achieves a mean-squared error of $O(H^2R_{\max}^2\sum_{t=1}^H\mathbb E_\mu[(w_{\pi,\mu}(s_t,a_t))^2]/n)$ for large $n$, where $w_{\pi,\mu}(s_t,a_t)$ is the ratio of the marginal distribution of $t$th step under $\pi$ and $\mu$, $H$ is the horizon, $R_{\max}$ is the maximal rewards, and $n$ is the sample size. The result nearly matches the Cramer-Rao lower bounds for DAG MDP in \citet{jiang2016doubly} for most non-trivial regimes. To the best of our knowledge, this is the first OPE estimator with provably optimal dependence in $H$ and the second moments of the importance weight. Besides theoretical optimality, we empirically demonstrate the superiority of our method in time-varying, partially observable, and long-horizon RL environments.

Includes members:
  Member "combining parametric and nonparametric models for off policy evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymptotically efficient off policy evaluation for tabular reinforcement learning")
  Member "off policy deep reinforcement learning by bootstrapping the covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "breaking the curse of horizon infinite horizon off policy estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "representation balancing mdps for off policy policy evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe counterfactual reinforcement learning")
  Member "more robust doubly robust off policy evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe counterfactual reinforcement learning")
  Member "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "using options and covariance testing for long horizon off policy policy evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "batch stationary distribution estimation")
  Member "data efficient off policy policy evaluation for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the fairness of randomized trials for recommendation with heterogeneous demographics and beyond")

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)