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: "stacked thompson bandits"
  Abstract: We introduce Stacked Thompson Bandits (STB) for efficiently generating plans that are likely to satisfy a given bounded temporal logic requirement. STB uses a simulation for evaluation of plans, and takes a Bayesian approach to using the resulting information to guide its search. In particular, we show that stacking multiarmed bandits and using Thompson sampling to guide the action selection process for each bandit enables STB to generate plans that satisfy requirements with a high probability while only searching a fraction of the search space.

Connected through hyperedge HE-1 (induced by source paper "memory bounded open loop planning in large pomdps using thompson sampling"):
  with members:
    - "leveraging statistical multi agent online planning with emergent value function approximation"
    - "open loop execution of tree search algorithms"
    - "further optimal regret bounds for thompson sampling"
    - "heuristic search value iteration for pomdps"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-2 (induced by source paper "adaptive thompson sampling stacks for memory bounded open loop planning"):
  with members:
    - "memory bounded open loop planning in large pomdps using thompson sampling"
    - "open loop execution of tree search algorithms"
    - "further optimal regret bounds for thompson sampling"
    - "heuristic search value iteration for pomdps"

Connected through hyperedge HE-3 (induced by source paper "leveraging statistical multi agent online planning with emergent value function approximation"):
  with members:
    - "scalable multiagent coordination with distributed online open loop planning"
    - "thinking fast and slow with deep learning and tree search"
    - "massively parallel methods for deep reinforcement learning"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "playing atari with deep reinforcement learning"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "optimal and approximate q value functions for decentralized pomdps"

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.SE(Software Engineering)