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: "policy optimization via importance sampling"
  Abstract: Policy optimization is an effective reinforcement learning approach to solve continuous control tasks. Recent achievements have shown that alternating online and offline optimization is a successful choice for efficient trajectory reuse. However, deciding when to stop optimizing and collect new trajectories is non-trivial, as it requires to account for the variance of the objective function estimate. In this paper, we propose a novel, model-free, policy search algorithm, POIS, applicable in both action-based and parameter-based settings. We first derive a high-confidence bound for importance sampling estimation; then we define a surrogate objective function, which is optimized offline whenever a new batch of trajectories is collected. Finally, the algorithm is tested on a selection of continuous control tasks, with both linear and deep policies, and compared with state-of-the-art policy optimization methods.

Connected through hyperedge HE-1 (induced by source paper "taylor expansion policy optimization"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "adaptive trade offs in off policy learning"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "policy optimization through approximated importance sampling"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"

Connected through hyperedge HE-2 (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"
    - "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 improvement for pomdps using normalized importance sampling"
    - "off policy actor critic"
    - "learning from scarce experience"

Connected through hyperedge HE-3 (induced by source paper "policy optimization with stochastic mirror descent"):
  with members:
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "trajectory wise control variates for variance reduction in policy gradient methods"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "addressing function approximation error in actor critic methods"
    - "predictor corrector policy optimization"
    - "stochastic variance reduced policy gradient"

Connected through hyperedge HE-4 (induced by source paper "sample efficient policy gradient methods with recursive variance reduction"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "policy optimization with stochastic mirror descent"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"

Connected through hyperedge HE-5 (induced by source paper "diagnosing bottlenecks in deep q learning algorithms"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "deep reinforcement learning and the deadly triad"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "the utility of sparse representations for control in reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-6 (induced by source paper "trajectory based off policy deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "parameter space noise for exploration"
    - "proximal policy optimization algorithms"
    - "deep exploration via bootstrapped dqn"
    - "safe and efficient off policy reinforcement learning"
    - "benchmarking deep reinforcement learning for continuous control"
    - "prioritized experience replay"

Connected through hyperedge HE-7 (induced by source paper "off policy policy gradient with state distribution correction"):
  with members:
    - "generalized off policy actor critic"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "soft actor critic algorithms and applications"
    - "more robust doubly robust off policy evaluation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "an improved convergence analysis of stochastic variance reduced policy gradient"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "stochastic variance reduced policy gradient"
    - "stochastic variance reduction for policy gradient estimation"
    - "stochastic variance reduction methods for policy evaluation"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "benchmarking deep reinforcement learning for continuous control"
    - "trust region policy optimization"
    - "stop wasting my gradients practical svrg"

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)