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: "discretized approximations for pomdp with average cost"
  Abstract: In this paper, we propose a new lower approximation scheme for POMDP with discounted and average cost criterion. The approximating functions are determined by their values at a finite number of belief points, and can be computed efficiently using value iteration algorithms for finite-state MDP. While for discounted problems several lower approximation schemes have been proposed earlier, ours seems the first of its kind for average cost problems. We focus primarily on the average cost case, and we show that the corresponding approximation can be computed efficiently using multi-chain algorithms for finite-state MDP. We give a preliminary analysis showing that regardless of the existence of the optimal average cost J in the POMDP, the approximation obtained is a lower bound of the liminf optimal average cost function, and can also be used to calculate an upper bound on the limsup optimal average cost function, as well as bounds on the cost of executing the stationary policy associated with the approximation. Weshow the convergence of the cost approximation, when the optimal average cost is constant and the optimal differential cost is continuous.

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"
    - "solving pomdps by searching the space of finite policies"
    - "a function approximation approach to estimation of policy gradient for pomdp with structured policies"

Connected through hyperedge HE-2 (induced by source paper "strategy synthesis in pomdps via game based abstractions"):
  with members:
    - "human in the loop synthesis for partially observable markov decision processes"
    - "counterexample guided abstraction refinement for pomdps"
    - "a storm is coming a modern probabilistic model checker"
    - "motion planning under partial observability using game based abstraction"
    - "reinforcement learning of pomdps using spectral methods"
    - "optimal cost almost sure reachability in pomdps"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "control of probabilistic systems under dynamic partially known environments with temporal logic specifications"

Connected through hyperedge HE-3 (induced by source paper "biased aggregation rollout and enhanced policy improvement for reinforcement learning"):
  with members:
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "the bottleneck simulator a model based deep reinforcement learning approach"
    - "value iteration with options and state aggregation"
    - "potential based shaping and q value initialization are equivalent"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "feature based aggregation and deep reinforcement learning a survey and some new implementations"):
  with members:
    - "the bottleneck simulator a model based deep reinforcement learning approach"
    - "deep reinforcement learning a brief survey"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deepchess end to end deep neural network for automatic learning in chess"
    - "deep learning in neural networks"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-5 (induced by source paper "hidden markov model estimation based q learning for partially observable markov decision process"):
  with members:
    - "qmdp net deep learning for planning under partial observability"
    - "a pac rl algorithm for episodic pomdps"
    - "deep recurrent q learning for partially observable mdps"
    - "memory based control with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "motion planning under partial observability using game based abstraction"):
  with members:
    - "counterexample guided abstraction refinement for pomdps"
    - "a storm is coming a modern probabilistic model checker"
    - "optimal cost almost sure reachability in pomdps"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "control of probabilistic systems under dynamic partially known environments with temporal logic specifications"
    - "partial observation stochastic games how to win when belief fails"

Connected through hyperedge HE-7 (induced by source paper "finite model approximations for partially observed markov decision processes with discounted cost"):
  with members:
    - "optimal quantization for distribution synthesis"
    - "point based pomdp algorithms improved analysis and implementation"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-8 (induced by source paper "infinite horizon optimal transmission power control for remote state estimation over fading channels"):
  with members:
    - "multi sensor scheduling for state estimation with event based stochastic triggers"
    - "on the existence of optimal policies for a class of static and sequential dynamic teams"
    - "distortion transmission trade off in real time transmission of markov sources"
    - "stochastic event triggered sensor scheduling for remote state estimation"
    - "design of state based schedulers for a network of control loops"
    - "optimal pruning for multi step sensor scheduling"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"
    - "paging and registration in cellular networks jointly optimal policies and an iterative algorithm"

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)