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: "using options and covariance testing for long horizon off policy policy evaluation"
  Abstract: Evaluating a policy by deploying it in the real world can be risky and costly. Off-policy policy evaluation (OPE) algorithms use historical data collected from running a previous policy to evaluate a new policy, which provides a means for evaluating a policy without requiring it to ever be deployed. Importance sampling is a popular OPE method because it is robust to partial observability and works with continuous states and actions. However, the amount of historical data required by importance sampling can scale exponentially with the horizon of the problem: the number of sequential decisions that are made. We propose using policies over temporally extended actions, called options, and show that combining these policies with importance sampling can significantly improve performance for long-horizon problems. In addition, we can take advantage of special cases that arise due to options-based policies to further improve the performance of importance sampling. We further generalize these special cases to a general covariance testing rule that can be used to decide which weights to drop in an IS estimate, and derive a new IS algorithm called Incremental Importance Sampling that can provide significantly more accurate estimates for a broad class of domains.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "estimating counterfactual treatment outcomes over time through adversarially balanced representations"):
  with members:
    - "learning optimal policies from observational data"
    - "bayesian inference of individualized treatment effects using multi task gaussian processes"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "a bayesian nonparametric approach for estimating individualized treatment response curves"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "adam a method for stochastic optimization"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "optimal off policy evaluation for reinforcement learning with marginalized importance sampling"):
  with members:
    - "combining parametric and nonparametric models for off policy evaluation"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "representation balancing mdps for off policy policy evaluation"
    - "more robust doubly robust off policy evaluation"
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "policy optimization via importance sampling"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "towards generalization and simplicity in continuous control"
    - "proximal policy optimization algorithms"
    - "sample efficient actor critic with experience replay"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "representation balancing mdps for off policy policy evaluation"):
  with members:
    - "more robust doubly robust off policy evaluation"
    - "learning optimal policies from observational data"
    - "bayesian inference of individualized treatment effects using multi task gaussian processes"
    - "bootstrapping with models confidence intervals for off policy evaluation"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "doubly robust policy evaluation and learning"
    - "on integral probability metrics φ divergences and binary classification"

Connected through hyperedge HE-6 (induced by source paper "breaking the curse of horizon infinite horizon off policy estimation"):
  with members:
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "learning from logged implicit exploration data"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-7 (induced by source paper "safe policy improvement with baseline bootstrapping"):
  with members:
    - "exploration by random network distillation"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "on ensuring that intelligent machines are well behaved"
    - "dynamic safe interruptibility for decentralized multi agent reinforcement learning"
    - "proximal policy optimization algorithms"
    - "unifying count based exploration and intrinsic motivation"
    - "benchmarking deep reinforcement learning for continuous control"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "eigenoption discovery through the deep successor representation"):
  with members:
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "adaptive skills adaptive partitions asap"
    - "the option critic architecture"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "the arcade learning environment an evaluation platform for general agents"

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.AI(Artificial Intelligence)