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: "flexible decomposition algorithms for weakly coupled markov decision problems"
  Abstract: This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these approaches, a large, stochastic decision problem is divided into smaller pieces. The first approach builds a cache of policies for each part of the problem independently, and then combines the pieces in a separate, light-weight step. A second approach also divides the problem into smaller pieces, but information is communicated between the different problem pieces, allowing intelligent decisions to be made about which piece requires the most attention. Both approaches can be used to find optimal policies or approximately optimal policies with provable bounds. These algorithms also provide a framework for the efficient transfer of knowledge across problems that share similar structure.

Connected through hyperedge HE-1 (induced by source paper "graying the black box understanding dqns"):
  with members:
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "prioritized experience replay"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "increasing the action gap new operators for reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning discovers internal models"):
  with members:
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "graying the black box understanding dqns"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "visualizing and understanding convolutional networks"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-3 (induced by source paper "solving transition independent multi agent mdps with sparse interactions extended version"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "optimal coordinated planning amongst self interested agents with private state"

Connected through hyperedge HE-4 (induced by source paper "topological value iteration algorithms"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "hierarchical solution of markov decision processes using macro actions"
    - "the complexity of decentralized control of markov decision processes"
    - "spudd stochastic planning using decision diagrams"
    - "region based incremental pruning for pomdps"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "symbolic generalization for on line planning"
    - "a heuristic search approach to planning with continuous resources in stochastic domains"

Connected through hyperedge HE-5 (induced by source paper "resource driven mission phasing techniques for constrained agents in stochastic environments"):
  with members:
    - "the complexity of decentralized control of markov decision processes"
    - "resource allocation among agents with mdp induced preferences"
    - "iterative milp methods for vehicle control problems"
    - "decentralized control of cooperative systems categorization and complexity analysis"
    - "efficient solution algorithms for factored mdps"
    - "multiagent control of self reconfigurable robots"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-6 (induced by source paper "restricted value iteration theory and algorithms"):
  with members:
    - "structured reachability analysis for markov decision processes"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "policy iteration for factored mdps"
    - "speeding up the convergence of value iteration in partially observable markov decision processes"
    - "value function approximations for partially observable markov decision processes"
    - "the computational complexity of probabilistic planning"
    - "a model approximation scheme for planning in partially observable stochastic domains"

Connected through hyperedge HE-7 (induced by source paper "probabilistic hybrid action models for predicting concurrent percept driven robot behavior"):
  with members:
    - "planning with external events"
    - "decision theoretic planning with concurrent temporally extended actions"
    - "probabilistic temporal reasoning with endogenous change"
    - "conformant planning via symbolic model checking"
    - "cc golog towards more realistic logic based robot controllers"
    - "decision theoretic planning structural assumptions and computational leverage"
    - "reasoning about noisy sensors and effectors in the situation calculus"

Connected through hyperedge HE-8 (induced by source paper "decision theoretic planning structural assumptions and computational leverage"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "exploiting the rule structure for decision making within the independent choice logic"
    - "integrating planning and execution in stochastic domains"
    - "abstracting probabilistic actions"
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "structured reachability analysis for markov decision processes"
    - "model reduction techniques for computing approximately optimal solutions for 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)