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: "off policy policy gradient with state distribution correction"
  Abstract: We study the problem of off-policy policy optimization in Markov decision processes, and develop a novel off-policy policy gradient method. Prior off-policy policy gradient approaches have generally ignored the mismatch between the distribution of states visited under the behavior policy used to collect data, and what would be the distribution of states under the learned policy. Here we build on recent progress for estimating the ratio of the state distributions under behavior and evaluation policies for policy evaluation, and present an off-policy policy gradient optimization technique that can account for this mismatch in distributions. We present an illustrative example of why this is important and a theoretical convergence guarantee for our approach. Empirically, we compare our method in simulations to several strong baselines which do not correct for this mismatch, significantly improving in the quality of the policy discovered.

Connected through hyperedge HE-1 (induced by source paper "an optimistic perspective on offline reinforcement learning"):
  with members:
    - "keep doing what worked behavioral modelling priors for offline reinforcement learning"
    - "benchmarking batch deep reinforcement learning algorithms"
    - "behavior regularized offline reinforcement learning"
    - "when to use parametric models in reinforcement learning"
    - "challenges of real world reinforcement learning"
    - "model based reinforcement learning for atari"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "a framework for data driven robotics"

Connected through hyperedge HE-2 (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 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"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

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"
    - "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"
    - "conditional importance sampling for off policy learning"

Connected through hyperedge HE-4 (induced by source paper "a nonparametric off policy policy gradient"):
  with members:
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "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-5 (induced by source paper "minimax optimal off policy evaluation with linear function approximation"):
  with members:
    - "asymptotically efficient off policy evaluation for tabular reinforcement learning"
    - "optimal off policy evaluation for reinforcement learning with marginalized importance sampling"
    - "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-6 (induced by source paper "optimizing medical treatment for sepsis in intensive care from reinforcement learning to pre trial evaluation"):
  with members:
    - "off policy deep reinforcement learning without exploration"
    - "representation balancing mdps for off policy policy evaluation"
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "safe and efficient off policy reinforcement learning"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-7 (induced by source paper "a nonparametric offpolicy policy gradient"):
  with members:
    - "off policy deep reinforcement learning without exploration"
    - "trust region policy optimization"
    - "policy improvement for pomdps using normalized importance sampling"
    - "off policy actor critic"
    - "learning from scarce experience"

Connected through hyperedge HE-8 (induced by source paper "way off policy batch deep reinforcement learning of implicit human preferences in dialog"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "off policy deep reinforcement learning without exploration"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "dialogue generation from imitation learning to inverse 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)