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: "multi step off policy learning without importance sampling ratios"
  Abstract: To estimate the value functions of policies from exploratory data, most model-free off-policy algorithms rely on importance sampling, where the use of importance sampling ratios often leads to estimates with severe variance. It is thus desirable to learn off-policy without using the ratios. However, such an algorithm does not exist for multi-step learning with function approximation. In this paper, we introduce the first such algorithm based on temporal-difference (TD) learning updates. We show that an explicit use of importance sampling ratios can be eliminated by varying the amount of bootstrapping in TD updates in an action-dependent manner. Our new algorithm achieves stability using a two-timescale gradient-based TD update. A prior algorithm based on lookup table representation called Tree Backup can also be retrieved using action-dependent bootstrapping, becoming a special case of our algorithm. In two challenging off-policy tasks, we demonstrate that our algorithm is stable, effectively avoids the large variance issue, and can perform substantially better than its state-of-the-art counterpart.

Connected through hyperedge HE-1 (induced by source paper "expected sarsa λ with control variate for variance reduction"):
  with members:
    - "per decision multi step temporal difference learning with control variates"
    - "finite sample analysis of the gtd policy evaluation algorithms in markov setting"
    - "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"
    - "finite sample analyses for td 0 with function approximation"
    - "convergent tree backup and retrace with function approximation"
    - "investigating practical linear temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "on convergence of emphatic temporal difference learning"

Connected through hyperedge HE-2 (induced by source paper "adaptive trade offs in off policy learning"):
  with members:
    - "understanding multi step deep reinforcement learning a systematic study of the dqn target"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "more robust doubly robust off policy evaluation"
    - "safe and efficient off policy reinforcement learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "importance resampling for off policy prediction"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "end to end learning for self driving cars"
    - "the predictron end to end learning and planning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "importance sampling with unequal support"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "prioritized experience replay"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "evolution guided policy gradient in reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "addressing function approximation error in actor critic methods"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"
    - "parameter space noise for exploration"

Connected through hyperedge HE-5 (induced by source paper "policy certificates towards accountable reinforcement learning"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "on polynomial time pac reinforcement learning with rich observations"
    - "is q learning provably efficient"
    - "fairness incentives for myopic agents"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "fairness in reinforcement learning"
    - "fairness in learning classic and contextual bandits"

Connected through hyperedge HE-6 (induced by source paper "online off policy prediction"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deep reinforcement learning that matters"
    - "stochastic variance reduction methods for policy evaluation"
    - "convergent tree backup and retrace with function approximation"
    - "on convergence of some gradient based temporal differences algorithms for off policy learning"
    - "sample efficient actor critic with experience replay"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "per decision multi step temporal difference learning with control variates"):
  with members:
    - "safe and efficient off policy reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "convergent tree backup and retrace with function approximation"):
  with members:
    - "finite sample analysis of the gtd policy evaluation algorithms in markov setting"
    - "linear stochastic approximation constant step size and iterate averaging"
    - "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"
    - "stochastic variance reduction methods for policy evaluation"
    - "stochastic variance reduction methods for saddle point problems"
    - "sample efficient actor critic with experience replay"
    - "asynchronous methods for deep reinforcement learning"
    - "an emphatic approach to the problem of off policy temporal difference 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)