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: "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
  Abstract: In many real-world reinforcement learning applications, access to the environment is limited to a fixed dataset, instead of direct (online) interaction with the environment. When using this data for either evaluation or training of a new policy, accurate estimates of discounted stationary distribution ratios -- correction terms which quantify the likelihood that the new policy will experience a certain state-action pair normalized by the probability with which the state-action pair appears in the dataset -- can improve accuracy and performance. In this work, we propose an algorithm, DualDICE, for estimating these quantities. In contrast to previous approaches, our algorithm is agnostic to knowledge of the behavior policy (or policies) used to generate the dataset. Furthermore, it eschews any direct use of importance weights, thus avoiding potential optimization instabilities endemic of previous methods. In addition to providing theoretical guarantees, we present an empirical study of our algorithm applied to off-policy policy evaluation and find that our algorithm significantly improves accuracy compared to existing techniques.

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"
    - "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 "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"
    - "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-3 (induced by source paper "stable policy optimization via off policy divergence regularization"):
  with members:
    - "imitation learning via off policy distribution matching"
    - "deep conservative policy iteration"
    - "truly proximal policy optimization"
    - "a theory of regularized markov decision processes"
    - "algaedice policy gradient from arbitrary experience"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "on convergence and stability of gans"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-4 (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"
    - "algaedice policy gradient from arbitrary experience"
    - "conditional importance sampling for off policy learning"

Connected through hyperedge HE-5 (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"
    - "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-6 (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"
    - "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-7 (induced by source paper "minimax optimal off policy evaluation with linear function approximation"):
  with members:
    - "asymptotically efficient off policy evaluation for tabular reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "optimal off policy evaluation for reinforcement learning with marginalized importance sampling"
    - "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-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"
    - "a theory of regularized markov decision processes"
    - "a kernel loss for solving the bellman equation"
    - "breaking the curse of horizon infinite horizon off policy estimation"

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)