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: "belief propagation for structured decision making"
  Abstract: Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or understanding exact and approximate inference. However, variational approaches have not been widely adoped for decision making in graphical models, often formulated through influence diagrams and including both centralized and decentralized (or multi-agent) decisions. In this work, we present a general variational framework for solving structured cooperative decision-making problems, use it to propose several belief propagation-like algorithms, and analyze them both theoretically and empirically.

Connected through hyperedge HE-1 (induced by source paper "model based multi agent reinforcement learning with cooperative prioritized sweeping"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-2 (induced by source paper "optimal control of complex systems through variational inference with a discrete event decision process"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "continuous control with deep reinforcement learning"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "new inference strategies for solving markov decision processes using reversible jump mcmc"
    - "efficient inference in markov control problems"

Connected through hyperedge HE-3 (induced by source paper "stochastic planning and lifted inference"):
  with members:
    - "the complexity of reasoning with fodd and gfodd"
    - "solving relational mdps with exogenous events and additive rewards"
    - "spudd stochastic planning using decision diagrams"
    - "policy iteration for relational mdps"
    - "first order decision diagrams for relational mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-4 (induced by source paper "deep value of information estimators for collaborative human machine information gathering"):
  with members:
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "end to end training of deep visuomotor policies"
    - "near optimal nonmyopic value of information in graphical models"
    - "robust interactive learning"
    - "finding approximate pomdp solutions through belief compression"

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)