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: "doubly robust off policy value evaluation for reinforcement learning"
  Abstract: We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem is often a critical step when applying RL in real-world problems. Despite its importance, existing general methods either have uncontrolled bias or suffer high variance. In this work, we extend the doubly robust estimator for bandits to sequential decision-making problems, which gets the best of both worlds: it is guaranteed to be unbiased and can have a much lower variance than the popular importance sampling estimators. We demonstrate the estimator's accuracy in several benchmark problems, and illustrate its use as a subroutine in safe policy improvement. We also provide theoretical results on the hardness of the problem, and show that our estimator can match the lower bound in certain scenarios.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning via fenchel rockafellar duality"):
  with members:
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "on the fairness of randomized trials for recommendation with heterogeneous demographics and beyond"):
  with members:
    - "average individual fairness algorithms generalization and experiments"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "policy learning for fairness in ranking"
    - "less is better unweighted data subsampling via influence function"
    - "delayed impact of fair machine learning"
    - "learning optimal fair policies"
    - "causal embeddings for recommendation"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

Connected through hyperedge HE-3 (induced by source paper "minimax confidence interval for off policy evaluation and policy optimization"):
  with members:
    - "reinforcement learning via fenchel rockafellar duality"
    - "gradientdice rethinking generalized offline estimation of stationary values"
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "understanding the curse of horizon in off policy evaluation via conditional importance sampling"
    - "information theoretic considerations in batch reinforcement learning"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "algaedice policy gradient from arbitrary experience"

Connected through hyperedge HE-4 (induced by source paper "safe counterfactual reinforcement learning"):
  with members:
    - "intrinsically efficient stable and bounded off policy evaluation for reinforcement learning"
    - "off policy evaluation via off policy classification"
    - "importance sampling policy evaluation with an estimated behavior policy"
    - "efficient counterfactual learning from bandit feedback"
    - "representation balancing mdps for off policy policy evaluation"
    - "more robust doubly robust off policy evaluation"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "data efficient off policy policy evaluation for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "asymptotically efficient off policy evaluation for tabular reinforcement learning"):
  with members:
    - "combining parametric and nonparametric models for off policy evaluation"
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "batch stationary distribution estimation"):
  with members:
    - "adversarial learning of a sampler based on an unnormalized distribution"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "online factorization and partition of complex networks from random walks"
    - "using options and covariance testing for long horizon off policy policy evaluation"
    - "learning from conditional distributions via dual embeddings"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "the fast convergence of incremental pca"

Connected through hyperedge HE-7 (induced by source paper "confounding robust policy evaluation in infinite horizon reinforcement learning"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "off policy evaluation in partially observable environments"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "deconfounding reinforcement learning in observational settings"
    - "confounding robust policy improvement"
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"
    - "deep reinforcement learning for sepsis treatment"

Connected through hyperedge HE-8 (induced by source paper "gradientdice rethinking generalized offline estimation of stationary values"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "provably convergent off policy actor critic with function approximation"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "addressing function approximation error in actor critic methods"
    - "learning from conditional distributions via dual embeddings"
    - "learning from conditional distributions via dual kernel embeddings"

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)