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: "double reinforcement learning for efficient off policy evaluation in markov decision processes"
  Abstract: Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. We consider for the first time the semiparametric efficiency limits of OPE in Markov decision processes (MDPs), where actions, rewards, and states are memoryless. We show existing OPE estimators may fail to be efficient in this setting. We develop a new estimator based on cross-fold estimation of $q$-functions and marginalized density ratios, which we term double reinforcement learning (DRL). We show that DRL is efficient when both components are estimated at fourth-root rates and is also doubly robust when only one component is consistent. We investigate these properties empirically and demonstrate the performance benefits due to harnessing memorylessness.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning via fenchel rockafellar duality"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "doubly robust bias reduction in infinite horizon off policy estimation"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "algaedice policy gradient from arbitrary experience"
    - "a divergence minimization perspective on imitation learning methods"
    - "imitation learning as f divergence minimization"
    - "exponential family estimation via adversarial dynamics embedding"

Connected through hyperedge HE-2 (induced by source paper "asymptotically efficient off policy evaluation for tabular reinforcement learning"):
  with members:
    - "combining parametric and nonparametric models for off policy evaluation"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "representation balancing mdps for off policy policy evaluation"
    - "is q learning provably efficient"
    - "more robust doubly robust off policy evaluation"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "data efficient off policy policy evaluation for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "empirical study of off policy policy evaluation for reinforcement learning"):
  with members:
    - "gradientdice rethinking generalized offline estimation of stationary values"
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "off policy evaluation in partially observable environments"
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "batch policy learning under constraints"
    - "information theoretic considerations in batch reinforcement learning"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"

Connected through hyperedge HE-4 (induced by source paper "off policy evaluation in partially observable environments"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "batch policy learning under constraints"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "evaluating reinforcement learning algorithms in observational health settings"
    - "deconfounding reinforcement learning in observational settings"
    - "contextual bandits with latent confounders an nmf approach"
    - "linguistically regularized lstms for sentiment classification"

Connected through hyperedge HE-5 (induced by source paper "minimax weight and q function learning for off policy evaluation"):
  with members:
    - "on value functions and the agent environment boundary"
    - "batch policy learning under constraints"
    - "information theoretic considerations in batch reinforcement learning"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "algaedice policy gradient from arbitrary experience"
    - "a kernel loss for solving the bellman equation"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "importance sampling policy evaluation with an estimated behavior policy"

Connected through hyperedge HE-6 (induced by source paper "doubly robust bias reduction in infinite horizon off policy estimation"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "a kernel loss for solving the bellman equation"
    - "importance sampling policy evaluation with an estimated behavior policy"
    - "representation balancing mdps for off policy policy evaluation"
    - "more robust doubly robust off policy evaluation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-7 (induced by source paper "understanding the curse of horizon in off policy evaluation via conditional importance sampling"):
  with members:
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "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)