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: "optimizing memory bounded controllers for decentralized pomdps"
  Abstract: We present a memory-bounded optimization approach for solving infinite-horizon decentralized POMDPs. Policies for each agent are represented by stochastic finite state controllers. We formulate the problem of optimizing these policies as a nonlinear program, leveraging powerful existing nonlinear optimization techniques for solving the problem. While existing solvers only guarantee locally optimal solutions, we show that our formulation produces higher quality controllers than the state-of-the-art approach. We also incorporate a shared source of randomness in the form of a correlation device to further increase solution quality with only a limited increase in space and time. Our experimental results show that nonlinear optimization can be used to provide high quality, concise solutions to decentralized decision problems under uncertainty.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for heterogeneous teams with palo bounds"):
  with members:
    - "deep recurrent q learning for partially observable mdps"

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"
    - "anytime planning for decentralized pomdps using expectation maximization"
    - "rollout sampling policy iteration for decentralized pomdps"
    - "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 "an investigation into mathematical programming for finite horizon decentralized pomdps"):
  with members:
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "a bilinear programming approach for multiagent planning"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-4 (induced by source paper "policy iteration for decentralized control of markov decision processes"):
  with members:
    - "learning to cooperate via policy search"
    - "solving pomdps by searching in policy space"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "planning with partially observable markov decision processes advances in exact solution method"
    - "point based pomdp algorithms improved analysis and implementation"
    - "region based incremental pruning for pomdps"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"

Connected through hyperedge HE-5 (induced by source paper "optimal and approximate q value functions for decentralized pomdps"):
  with members:
    - "learning to cooperate via policy search"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "mixed integer linear programming for exact finite horizon planning in decentralized pomdps"
    - "perseus randomized point based value iteration for pomdps"
    - "a framework for sequential planning in multi agent settings"
    - "decentralized control of cooperative systems categorization and complexity analysis"
    - "efficient solution algorithms for factored mdps"

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)