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 deep reinforcement learning by bootstrapping the covariate shift"
  Abstract: In this paper we revisit the method of off-policy corrections for reinforcement learning (COP-TD) pioneered by Hallak et al. (2017). Under this method, online updates to the value function are reweighted to avoid divergence issues typical of off-policy learning. While Hallak et al.'s solution is appealing, it cannot easily be transferred to nonlinear function approximation. First, it requires a projection step onto the probability simplex; second, even though the operator describing the expected behavior of the off-policy learning algorithm is convergent, it is not known to be a contraction mapping, and hence, may be more unstable in practice. We address these two issues by introducing a discount factor into COP-TD. We analyze the behavior of discounted COP-TD and find it better behaved from a theoretical perspective. We also propose an alternative soft normalization penalty that can be minimized online and obviates the need for an explicit projection step. We complement our analysis with an empirical evaluation of the two techniques in an off-policy setting on the game Pong from the Atari domain where we find discounted COP-TD to be better behaved in practice than the soft normalization penalty. Finally, we perform a more extensive evaluation of discounted COP-TD in 5 games of the Atari domain, where we find performance gains for our approach.

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"
    - "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-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"
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "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 "batch stationary distribution estimation"):
  with members:
    - "adversarial learning of a sampler based on an unnormalized distribution"
    - "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"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "the fast convergence of incremental pca"

Connected through hyperedge HE-4 (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"
    - "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"
    - "data efficient off policy policy evaluation for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "gradientdice rethinking generalized offline estimation of stationary values"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "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"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "off policy policy gradient with state distribution correction"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "deepmdp learning continuous latent space models for representation learning"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "model based reinforcement learning for atari"
    - "the value function polytope in reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"

Connected through hyperedge HE-8 (induced by source paper "generalized off policy actor critic"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "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"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "online off policy prediction"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

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)