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 the space of finite policies"
  Abstract: Solving partially observable Markov decision processes (POMDPs) is highly intractable in general, at least in part because the optimal policy may be infinitely large. In this paper, we explore the problem of finding the optimal policy from a restricted set of policies, represented as finite state automata of a given size. This problem is also intractable, but we show that the complexity can be greatly reduced when the POMDP and/or policy are further constrained. We demonstrate good empirical results with a branch-and-bound method for finding globally optimal deterministic policies, and a gradient-ascent method for finding locally optimal stochastic policies.

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 in policy space"
    - "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 "counterexample driven synthesis for probabilistic program sketches"):
  with members:
    - "shepherding hordes of markov chains"
    - "structured synthesis for probabilistic systems"
    - "safety aware apprenticeship learning"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "high level counterexamples for probabilistic automata"
    - "safety constrained reinforcement learning for mdps"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"

Connected through hyperedge HE-3 (induced by source paper "a correctness result for synthesizing plans with loops in stochastic domains"):
  with members:
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"

Connected through hyperedge HE-4 (induced by source paper "human in the loop synthesis for partially observable markov decision processes"):
  with members:
    - "permissive finite state controllers of pomdps using parameter synthesis"
    - "a storm is coming a modern probabilistic model checker"
    - "motion planning under partial observability using game based abstraction"
    - "synthesis of shared control protocols with provable safety and performance guarantees"
    - "high level counterexamples for probabilistic automata"
    - "optimal cost almost sure reachability in pomdps"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "heuristic search value iteration for pomdps"

Connected through hyperedge HE-5 (induced by source paper "permissive finite state controllers of pomdps using parameter synthesis"):
  with members:
    - "parametric markov chains pctl complexity and fraction free gaussian elimination"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "safety constrained reinforcement learning for mdps"
    - "permissive controller synthesis for probabilistic systems"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "optimal cost almost sure reachability in pomdps"

Connected through hyperedge HE-6 (induced by source paper "stochastic shortest path with energy constraints in pomdps"):
  with members:
    - "counterexample explanation by learning small strategies in markov decision processes"
    - "optimal cost almost sure reachability in pomdps"
    - "efficient controller synthesis for consumption games with multiple resource types"
    - "heuristic search value iteration for pomdps"
    - "energy and mean payoff parity markov decision processes"
    - "qualitative analysis of partially observable markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "minimum information lqg control part ii retentive controllers"):
  with members:
    - "minimum information lqg control part i memoryless controllers"
    - "optimal selective attention in reactive agents"
    - "bounded planning in passive pomdps"

Connected through hyperedge HE-8 (induced by source paper "pomdps under probabilistic semantics"):
  with members:
    - "my brain is full when more memory helps"
    - "learning finite state controllers for partially observable environments"
    - "heuristic search value iteration for pomdps"
    - "randomness for free"
    - "qualitative analysis of partially observable markov decision processes"
    - "algorithms for omega regular games with imperfect information"
    - "interactive small step algorithms ii abstract state machines and the characterization theorem"
    - "the computational complexity of probabilistic planning"

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)