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: "solving pomdps by searching in policy space"
  Abstract: Most algorithms for solving POMDPs iteratively improve a value function that implicitly represents a policy and are said to search in value function space. This paper presents an approach to solving POMDPs that represents a policy explicitly as a finite-state controller and iteratively improves the controller by search in policy space. Two related algorithms illustrate this approach. The first is a policy iteration algorithm that can outperform value iteration in solving infinitehorizon POMDPs. It provides the foundation for a new heuristic search algorithm that promises further speedup by focusing computational effort on regions of the problem space that are reachable, or likely to be reached, from a start state.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems"):
  with members:
    - "active rendezvous for multi robot pose graph optimization using sensing over wi fi"
    - "the limits and potentials of deep learning for robotics"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "solving pomdps by searching the space of finite policies"
    - "a function approximation approach to estimation of policy gradient for pomdp with structured policies"
    - "discretized approximations for pomdp with average cost"

Connected through hyperedge HE-2 (induced by source paper "risk averse planning under uncertainty"):
  with members:
    - "stochastic finite state control of pomdps with ltl specifications"
    - "safe policy synthesis in multi agent pomdps via discrete time barrier functions"
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "algorithms for cvar optimization in mdps"
    - "probabilistic planning for continuous dynamic systems under bounded risk"
    - "sparse stochastic finite state controllers for pomdps"

Connected through hyperedge HE-3 (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"
    - "improved memory bounded dynamic programming for decentralized pomdps"

Connected through hyperedge HE-4 (induced by source paper "learning finite state controllers for partially observable environments"):
  with members:
    - "solving pomdps by searching the space of finite policies"
    - "learning policies with external memory"
    - "decision theoretic planning structural assumptions and computational leverage"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "the complexity of decentralized control of markov decision processes"):
  with members:
    - "learning to cooperate via policy search"
    - "my brain is full when more memory helps"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "solving pomdps by searching the space of finite policies"

Connected through hyperedge HE-6 (induced by source paper "solving pomdps by searching the space of finite policies"):
  with members:
    - "learning finite state controllers for partially observable environments"
    - "learning policies with external memory"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-7 (induced by source paper "my brain is full when more memory helps"):
  with members:
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"

Connected through hyperedge HE-8 (induced by source paper "maa a heuristic search algorithm for solving decentralized pomdps"):
  with members:
    - "learning to cooperate via policy search"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "solving pomdps by searching the space of finite policies"
    - "decentralized control of cooperative systems categorization and complexity analysis"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"
    - "value function approximations for partially observable markov decision processes"

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)