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 learning with eligibility traces a survey"
  Abstract: In the framework of Markov Decision Processes, off-policy learning, that is the problem of learning a linear approximation of the value function of some fixed policy from one trajectory possibly generated by some other policy. We briefly review on-policy learning algorithms of the literature (gradient-based and least-squares-based), adopting a unified algorithmic view. Then, we highlight a systematic approach for adapting them to off-policy learning with eligibility traces. This leads to some known algorithms - off-policy LSTD(\lambda), LSPE(\lambda), TD(\lambda), TDC/GQ(\lambda) - and suggests new extensions - off-policy FPKF(\lambda), BRM(\lambda), gBRM(\lambda), GTD2(\lambda). We describe a comprehensive algorithmic derivation of all algorithms in a recursive and memory-efficent form, discuss their known convergence properties and illustrate their relative empirical behavior on Garnet problems. Our experiments suggest that the most standard algorithms on and off-policy LSTD(\lambda)/LSPE(\lambda) - and TD(\lambda) if the feature space dimension is too large for a least-squares approach - perform the best.

Connected through hyperedge HE-1 (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"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "a theory of regularized markov decision processes"
    - "a kernel loss for solving the bellman equation"

Connected through hyperedge HE-2 (induced by source paper "learning transferable domain priors for safe exploration in reinforcement learning"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "diverse exploration for fast and safe policy improvement"
    - "sample efficient reinforcement learning through transfer and architectural priors"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "investigating human priors for playing video games"
    - "kickstarting deep reinforcement learning"
    - "universal successor representations for transfer reinforcement learning"
    - "an optimal online method of selecting source policies for reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "multi step off policy learning without importance sampling ratios"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "exploration enhanced politex"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "deepmind control suite"
    - "large scale study of curiosity driven learning"
    - "model free linear quadratic control via reduction to expert prediction"
    - "distributed prioritized experience replay"
    - "is q learning provably efficient"
    - "multi step reinforcement learning a unifying algorithm"
    - "prioritized experience replay"

Connected through hyperedge HE-5 (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"
    - "multi step off policy learning without importance sampling ratios"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "adaptive temporal difference learning for policy evaluation with per state uncertainty estimates"):
  with members:
    - "temporal difference learning with neural networks study of the leakage propagation problem"
    - "efficient exploration through bayesian deep q networks"
    - "deep exploration via bootstrapped dqn"
    - "adaptive lambda least squares temporal difference learning"
    - "a greedy approach to adapting the trace parameter for temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view"

Connected through hyperedge HE-7 (induced by source paper "experience replay using transition sequences"):
  with members:
    - "selective experience replay for lifelong learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "hindsight experience replay"
    - "identification and off policy learning of multiple objectives using adaptive clustering"
    - "swarm enabling technology for multi robot systems"
    - "sample efficient actor critic with experience replay"
    - "asynchronous methods for deep reinforcement learning"
    - "data efficient off policy policy evaluation for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "qualitative measurements of policy discrepancy for return based deep q network"):
  with members:
    - "applications of deep learning and reinforcement learning to biological data"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "noisy networks for exploration"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
    - "averaged dqn variance reduction and stabilization for deep reinforcement learning"
    - "sample efficient actor critic with experience replay"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "q prop sample efficient policy gradient with an off policy critic"

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)