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: "breaking the curse of horizon infinite horizon off policy estimation"
  Abstract: We consider the off-policy estimation problem of estimating the expected reward of a target policy using samples collected by a different behavior policy. Importance sampling (IS) has been a key technique to derive (nearly) unbiased estimators, but is known to suffer from an excessively high variance in long-horizon problems. In the extreme case of in infinite-horizon problems, the variance of an IS-based estimator may even be unbounded. In this paper, we propose a new off-policy estimation method that applies IS directly on the stationary state-visitation distributions to avoid the exploding variance issue faced by existing estimators.Our key contribution is a novel approach to estimating the density ratio of two stationary distributions, with trajectories sampled from only the behavior distribution. We develop a mini-max loss function for the estimation problem, and derive a closed-form solution for the case of RKHS. We support our method with both theoretical and empirical analyses.

Connected through hyperedge HE-1 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation"):
  with members:
    - "gradientdice rethinking generalized offline estimation of stationary values"
    - "a nonparametric offpolicy policy gradient"
    - "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"
    - "algaedice policy gradient from arbitrary experience"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-2 (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-3 (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"
    - "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"
    - "data efficient off policy policy evaluation for reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "addressing function approximation error in actor critic methods"
    - "learning from conditional distributions via dual embeddings"
    - "learning from conditional distributions via dual kernel embeddings"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a nonparametric off policy policy gradient"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "policy optimization via importance sampling"
    - "policy improvement for pomdps using normalized importance sampling"
    - "off policy actor critic"
    - "learning from scarce experience"

Connected through hyperedge HE-6 (induced by source paper "adaptive estimator selection for off policy evaluation"):
  with members:
    - "doubly robust off policy evaluation with shrinkage"
    - "empirical study of off policy policy evaluation for reinforcement learning"
    - "more robust doubly robust off policy evaluation"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "safe and efficient off policy reinforcement learning"
    - "off policy evaluation for slate recommendation"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "a reinforcement learning framework for time dependent causal effects evaluation in a b testing"):
  with members:
    - "finite sample analysis for sarsa with linear function approximation"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "more robust doubly robust off policy evaluation"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "algaedice policy gradient from arbitrary experience"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "doubly robust bias reduction in infinite horizon off policy estimation"
    - "imitation learning via off policy distribution matching"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "a theory of regularized markov decision processes"
    - "a kernel loss for solving the bellman equation"

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)