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: "improved memory bounded dynamic programming for decentralized pomdps"
  Abstract: Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective in solving decentralized POMDPs with large horizons. We generalize the algorithm and improve its scalability by reducing the complexity with respect to the number of observations from exponential to polynomial. We derive error bounds on solution quality with respect to this new approximation and analyze the convergence behavior. To evaluate the effectiveness of the improvements, we introduce a new, larger benchmark problem. Experimental results show that despite the high complexity of decentralized POMDPs, scalable solution techniques such as MBDP perform surprisingly well.

Connected through hyperedge HE-1 (induced by source paper "on solving cooperative marl problems with a few good experiences"):
  with members:
    - "learning existing social conventions via observationally augmented self play"
    - "self imitation learning"
    - "counterfactual multi agent policy gradients"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-2 (induced by source paper "openspiel a framework for reinforcement learning in games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "rethinking formal models of partially observable multiagent decision making"
    - "behaviour suite for reinforcement learning"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "open ended learning in symmetric zero sum games"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"

Connected through hyperedge HE-3 (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"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "optimal control strategies in delayed sharing information structures"
    - "policy iteration for decentralized control of markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "intelligent knowledge distribution constrained action pomdps for resource aware multi agent communication"):
  with members:
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "solving pomdps by searching in policy space"

Connected through hyperedge HE-5 (induced by source paper "near optimal adversarial policy switching for decentralized asynchronous multi agent systems"):
  with members:
    - "ssd single shot multibox detector"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"
    - "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents"
    - "the complexity of decentralized control of markov decision processes"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "a framework for sequential planning in multi agent settings"

Connected through hyperedge HE-6 (induced by source paper "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"):
  with members:
    - "learning finite state controllers for partially observable environments"
    - "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents"
    - "exploiting model equivalences for solving interactive dynamic influence diagrams"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "rollout sampling policy iteration for decentralized pomdps"):
  with members:
    - "learning to cooperate via policy search"
    - "the complexity of decentralized control of markov decision processes"
    - "rollout sampling approximate policy iteration"

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)