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: "rollout sampling policy iteration for decentralized pomdps"
  Abstract: We present decentralized rollout sampling policy iteration (DecRSPI) - a new algorithm for multi-agent decision problems formalized as DEC-POMDPs. DecRSPI is designed to improve scalability and tackle problems that lack an explicit model. The algorithm uses Monte- Carlo methods to generate a sample of reachable belief states. Then it computes a joint policy for each belief state based on the rollout estimations. A new policy representation allows us to represent solutions compactly. The key benefits of the algorithm are its linear time complexity over the number of agents, its bounded memory usage and good solution quality. It can solve larger problems that are intractable for existing planning algorithms. Experimental results confirm the effectiveness and scalability of the approach.

Connected through hyperedge HE-1 (induced by source paper "deep decentralized multi task multi agent reinforcement learning under partial observability"):
  with members:
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "learning to communicate to solve riddles with deep distributed recurrent q networks"
    - "deep recurrent q learning for partially observable mdps"
    - "stick breaking policy learning in dec pomdps"
    - "adam a method for stochastic optimization"
    - "learning to cooperate via policy search"
    - "sample complexity of multi task reinforcement learning"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-2 (induced by source paper "incremental clustering and expansion for faster optimal planning in dec pomdps"):
  with members:
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "optimizing memory bounded controllers for decentralized pomdps"
    - "anytime planning for decentralized pomdps using expectation maximization"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "policy iteration for decentralized control of markov decision processes"
    - "online planning algorithms for pomdps"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-3 (induced by source paper "exploiting agent and type independence in collaborative graphical bayesian games"):
  with members:
    - "graphical models for game theory"
    - "tractable inference for complex stochastic processes"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "policy iteration for factored mdps"
    - "exploiting structure in cooperative bayesian games"
    - "map estimation message passing and perfect graphs"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "factored value iteration converges"

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)