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: "speeding up the convergence of value iteration in partially observable markov decision processes"
  Abstract: Partially observable Markov decision processes (POMDPs) have recently become popular among many AI researchers because they serve as a natural model for planning under uncertainty. Value iteration is a well-known algorithm for finding optimal policies for POMDPs. It typically takes a large number of iterations to converge. This paper proposes a method for accelerating the convergence of value iteration. The method has been evaluated on an array of benchmark problems and was found to be very effective: It enabled value iteration to converge after only a few iterations on all the test problems.

Connected through hyperedge HE-1 (induced by source paper "artificial intelligence for low resource communities influence maximization in an uncertain world"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "activating the breakfast club modeling influence spread in natural world social networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "maximizing social influence in nearly optimal time"
    - "community structure and scale free collections of erdős renyi graphs"
    - "information theoretic measures of influence based on content dynamics"

Connected through hyperedge HE-2 (induced by source paper "throughput maximization for ambient backscatter communication a reinforcement learning approach"):
  with members:
    - "reusing wireless power transfer for backscatter assisted cooperation in wpcn"
    - "backscatter assisted relaying in wireless powered communications network"
    - "wireless powered communication networks an overview"

Connected through hyperedge HE-3 (induced by source paper "stochastic 2 d motion planning with a pomdp framework"):
  with members:
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "point based pomdp algorithms improved analysis and implementation"
    - "heuristic search value iteration for pomdps"
    - "online planning algorithms for pomdps"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "despot online pomdp planning with regularization"):
  with members:
    - "bandit algorithms for tree search"
    - "pegasus a policy search method for large mdps and pomdps"
    - "point based pomdp algorithms improved analysis and implementation"
    - "heuristic search value iteration for pomdps"
    - "monte carlo bayesian reinforcement learning"
    - "online planning algorithms for pomdps"
    - "perseus randomized point based value iteration for pomdps"
    - "nonapproximability results for partially observable markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "supervisor synthesis of pomdp based on automata learning"):
  with members:
    - "counterexample guided abstraction refinement for pomdps"
    - "a storm is coming a modern probabilistic model checker"
    - "permissive controller synthesis for probabilistic systems"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "replanning in domains with partial information and sensing actions"
    - "randomness for free"

Connected through hyperedge HE-6 (induced by source paper "heuristic search value iteration for pomdps"):
  with members:
    - "integrating planning and execution in stochastic domains"
    - "structured reachability analysis for markov decision processes"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "spudd stochastic planning using decision diagrams"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "point based pomdp algorithms improved analysis and implementation"):
  with members:
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "heuristic search value iteration for pomdps"
    - "perseus randomized point based value iteration for pomdps"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-8 (induced by source paper "suboptimality bounds for stochastic shortest path problems"):
  with members:
    - "heuristic search value iteration for pomdps"

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)