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: "approximate planning for factored pomdps using belief state simplification"
  Abstract: We are interested in the problem of planning for factored POMDPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for factored POMDPs that exploits the accuracy-efficiency tradeoff in the belief state simplification introduced by Boyen and Koller.

Connected through hyperedge HE-1 (induced by source paper "sparse tree search optimality guarantees in pomdps with continuous observation spaces"):
  with members:
    - "policy optimization via importance sampling"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "online algorithms for pomdps with continuous state action and observation spaces"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "efficient decision making and belief space planning using sparse approximations"):
  with members:
    - "complexity analysis and efficient measurement selection primitives for high rate graph slam"
    - "tractable inference for complex stochastic processes"
    - "sampling based algorithms for optimal motion planning"
    - "finding approximate pomdp solutions through belief compression"

Connected through hyperedge HE-3 (induced by source paper "deep variational reinforcement learning for pomdps"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "filtering variational objectives"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "qmdp net deep learning for planning under partial observability"
    - "asynchronous methods for deep reinforcement learning"
    - "value iteration networks"
    - "playing fps games with deep reinforcement learning"
    - "importance weighted autoencoders"

Connected through hyperedge HE-4 (induced by source paper "bayesian reinforcement learning in factored pomdps"):
  with members:
    - "deep variational reinforcement learning for pomdps"
    - "learning in pomdps with monte carlo tree search"
    - "efficient exploration through bayesian deep q networks"
    - "on improving deep reinforcement learning for pomdps"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "the actor search tree critic astc for off policy pomdp learning in medical decision making"):
  with members:
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"
    - "a survey on deep learning in medical image analysis"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "heuristic search value iteration for pomdps"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-6 (induced by source paper "robotic manipulation of multiple objects as a pomdp"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-7 (induced by source paper "value directed belief state approximation for pomdps"):
  with members:
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "tractable inference for complex stochastic processes"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "spudd stochastic planning using decision diagrams"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-8 (induced by source paper "fast planning in stochastic games"):
  with members:
    - "continuous value function approximation for sequential bidding policies"

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)