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: "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.

Connected through hyperedge HE-1 (induced by source paper "minimax optimal off policy evaluation with linear function approximation"):
  with members:
    - "asymptotically efficient off policy evaluation for tabular reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "modelling transition dynamics in mdps with rkhs embeddings"

Connected through hyperedge HE-2 (induced by source paper "ctrl z recovering from instability in reinforcement learning"):
  with members:
    - "learning gentle object manipulation with curiosity driven deep reinforcement learning"
    - "off policy evaluation via off policy classification"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
    - "progressive neural networks"

Connected through hyperedge HE-3 (induced by source paper "triply robust off policy evaluation"):
  with members:
    - "doubly robust off policy evaluation with shrinkage"
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "robust regression for safe exploration in control"
    - "batch policy learning under constraints"
    - "robust covariate shift prediction with general losses and feature views"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "hierarchical exploration for accelerating contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "double reinforcement learning for efficient off policy evaluation in markov decision processes"):
  with members:
    - "intrinsically efficient stable and bounded off policy evaluation for reinforcement learning"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "more robust doubly robust off policy evaluation"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "safe and efficient off policy reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"

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)