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 reward shaping with ensembles"
  Abstract: Potential-based reward shaping (PBRS) is an e ective andpopular technique to speed up reinforcement learning byleveraging domain knowledge. While PBRS is proven toalways preserve optimal policies, its e ect on learning speedis determined by the quality of its potential function, which,in turn, depends on both the underlying heuristic and thescale. Knowing which heuristic will prove e ective requirestesting the options beforehand, and determining the appro-priate scale requires tuning, both of which introduce addi-tional sample complexity.We formulate a PBRS framework that reduces learningspeed, but does not incur extra sample complexity. For this,we propose to simultaneously learn an ensemble of policies,shaped w.r.t. many heuristics and on a range of scales. Thetarget policy is then obtained by voting. The ensemble needsto be able to eciently and reliably learn o -policy: re-quirements ful lled by the recent Horde architecture, whichwe take as our basis. We demonstrate empirically that (1)our ensemble policy outperforms both the base policy, andits single-heuristic components, and (2) an ensemble over ageneral range of scales performs at least as well as one withoptimally tuned components.

Connected through hyperedge HE-1 (induced by source paper "multi advisor reinforcement learning"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "separation of concerns in reinforcement learning"
    - "learning values across many orders of magnitude"
    - "adam a method for stochastic optimization"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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)