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: "policy iteration for decentralized control of markov decision processes"
  Abstract: Coordination of distributed agents is required for problems arising in many areas, including multi-robot systems, networking and e-commerce. As a formal framework for such problems, we use the decentralized partially observable Markov decision process (DECPOMDP). Though much work has been done on optimal dynamic programming algorithms for the single-agent version of the problem, optimal algorithms for the multiagent case have been elusive. The main contribution of this paper is an optimal policy iteration algorithm for solving DEC-POMDPs. The algorithm uses stochastic finite-state controllers to represent policies. The solution can include a correlation device, which allows agents to correlate their actions without communicating. This approach alternates between expanding the controller and performing value-preserving transformations, which modify the controller without sacrificing value. We present two efficient value-preserving transformations: one can reduce the size of the controller and the other can improve its value while keeping the size fixed. Empirical results demonstrate the usefulness of value-preserving transformations in increasing value while keeping controller size to a minimum. To broaden the applicability of the approach, we also present a heuristic version of the policy iteration algorithm, which sacrifices convergence to optimality. This algorithm further reduces the size of the controllers at each step by assuming that probability distributions over the other agents' actions are known. While this assumption may not hold in general, it helps produce higher quality solutions in our test problems.

Connected through hyperedge HE-1 (induced by source paper "distributed policy iteration for scalable approximation of cooperative multi agent policies"):
  with members:
    - "leveraging statistical multi agent online planning with emergent value function approximation"
    - "open loop execution of tree search algorithms"
    - "machine theory of mind"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "feedback based tree search for reinforcement learning"
    - "a deep policy inference q network for multi agent systems"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"

Connected through hyperedge HE-2 (induced by source paper "online planning for decentralized stochastic control with partial history sharing"):
  with members:
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "decentralized stochastic control with partial history sharing a common information approach"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "optimal control strategies in delayed sharing information structures"

Connected through hyperedge HE-3 (induced by source paper "q cp learning action values for cooperative planning"):
  with members:
    - "cooperative inverse reinforcement learning"
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-4 (induced by source paper "semantic level decentralized multi robot decision making using probabilistic macro observations"):
  with members:
    - "hierarchical bayesian noise inference for robust real time probabilistic object classification"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "risk and regret of hierarchical bayesian learners"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "scalable accelerated decentralized multi robot policy search in continuous observation spaces"):
  with members:
    - "decentralized control of partially observable markov decision processes using belief space macro actions"

Connected through hyperedge HE-6 (induced by source paper "real time rescheduling in distributed railway network an agent based approach"):
  with members:
    - "the complexity of decentralized control of markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "stick breaking policy learning in dec pomdps"):
  with members:
    - "planning for decentralized control of multiple robots under uncertainty"
    - "anytime planning for decentralized pomdps using expectation maximization"

Connected through hyperedge HE-8 (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"
    - "rollout sampling policy iteration for decentralized pomdps"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "online planning algorithms for 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.AI(Artificial Intelligence)