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: "maa a heuristic search algorithm for solving decentralized pomdps"
  Abstract: We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, `or distributed resource allocation. Solving such problems efiectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.

Connected through hyperedge HE-1 (induced by source paper "a deep actor critic reinforcement learning framework for dynamic multichannel access"):
  with members:
    - "reinforcement learning based resource allocation in fog radio access networks for various iot environments"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"
    - "heuristic search value iteration for pomdps"

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"
    - "optimal control strategies in delayed sharing information structures"
    - "policy iteration for decentralized control of markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "information gathering in decentralized pomdps by policy graph improvement"):
  with members:
    - "multi robot active information gathering with periodic communication"
    - "incremental clustering and expansion for faster optimal planning in dec pomdps"

Connected through hyperedge HE-4 (induced by source paper "leveraging statistical multi agent online planning with emergent value function approximation"):
  with members:
    - "scalable multiagent coordination with distributed online open loop planning"
    - "stacked thompson bandits"
    - "thinking fast and slow with deep learning and tree search"
    - "massively parallel methods for deep reinforcement learning"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "playing atari with deep reinforcement learning"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-5 (induced by source paper "a framework for rate efficient control of distributed discrete systems"):
  with members:
    - "interactive scalar quantization for distributed resource allocation"
    - "overhead performance tradeoffs a resource allocation perspective"
    - "distributed function computation over a rooted directed tree"
    - "the complexity of decentralized control of markov decision processes"
    - "anytime reliable codes for stabilizing plants over erasure channels"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "rateless coding for gaussian channels"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-6 (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"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "a framework for sequential planning in multi agent settings"

Connected through hyperedge HE-7 (induced by source paper "multi robot active information gathering with periodic communication"):
  with members:
    - "planning for robotic exploration based on forward simulation"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-8 (induced by source paper "influence optimistic local values for multiagent planning extended version"):
  with members:
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "decision theoretic planning structural assumptions and computational leverage"

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)