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: "bayesian reinforcement learning in factored pomdps"
  Abstract: Bayesian approaches provide a principled solution to the exploration-exploitation trade-off in Reinforcement Learning. Typical approaches, however, either assume a fully observable environment or scale poorly. This work introduces the Factored Bayes-Adaptive POMDP model, a framework that is able to exploit the underlying structure while learning the dynamics in partially observable systems. We also present a belief tracking method to approximate the joint posterior over state and model variables, and an adaptation of the Monte-Carlo Tree Search solution method, which together are capable of solving the underlying problem near-optimally. Our method is able to learn efficiently given a known factorization or also learn the factorization and the model parameters at the same time. We demonstrate that this approach is able to outperform current methods and tackle problems that were previously infeasible.

Connected through hyperedge HE-1 (induced by source paper "inducing cooperation via team regret minimization based multi agent deep reinforcement learning"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "actor attention critic for multi agent reinforcement learning"
    - "deep counterfactual regret minimization"
    - "deep variational reinforcement learning for pomdps"

Connected through hyperedge HE-2 (induced by source paper "combining offline models and online monte carlo tree search for planning from scratch"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "despot online pomdp planning with regularization"
    - "bayesian reinforcement learning a survey"
    - "efficient learning and planning with compressed predictive states"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "heuristic search value iteration for pomdps"
    - "learning to make predictions in partially observable environments without a generative model"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-3 (induced by source paper "online learning and planning in partially observable domains without prior knowledge"):
  with members:
    - "combining offline models and online monte carlo tree search for planning from scratch"
    - "learning in pomdps with monte carlo tree search"
    - "bayesian reinforcement learning a survey"
    - "efficient learning and planning with compressed predictive states"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "heuristic search value iteration for pomdps"
    - "online planning algorithms for pomdps"
    - "a spectral algorithm for learning hidden markov models"

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)