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 factored mdps with hybrid state and action variables"
  Abstract: Efficient representations and solutions for large decision problems with continuous and discrete variables are among the most important challenges faced by the designers of automated decision support systems. In this paper, we describe a novel hybrid factored Markov decision process (MDP) model that allows for a compact representation of these problems, and a new hybrid approximate linear programming (HALP) framework that permits their efficient solutions. The central idea of HALP is to approximate the optimal value function by a linear combination of basis functions and optimize its weights by linear programming. We analyze both theoretical and computational aspects of this approach, and demonstrate its scale-up potential on several hybrid optimization problems.

Connected through hyperedge HE-1 (induced by source paper "scalable nonlinear planning with deep neural network learned transition models"):
  with members:
    - "compact and efficient encodings for planning in factored state and action spaces with learned binarized neural network transition models"
    - "scalable planning with tensorflow for hybrid nonlinear domains"
    - "understanding deep learning requires rethinking generalization"
    - "densely connected convolutional networks"
    - "a hybrid lp rpg heuristic for modelling numeric resource flows in planning"
    - "playing atari with deep reinforcement learning"
    - "do deep nets really need to be deep"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-2 (induced by source paper "hybrid planning for dynamic multimodal stochastic shortest paths"):
  with members:
    - "dynamic real time multimodal routing with hierarchical hybrid planning"
    - "an anytime algorithm for task and motion mdps"
    - "benchmarking deep reinforcement learning for continuous control"
    - "hierarchical solution of markov decision processes using macro actions"
    - "the ff planning system fast plan generation through heuristic search"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-3 (induced by source paper "an online prediction algorithm for reinforcement learning with linear function approximation using cross entropy method"):
  with members:
    - "a cross entropy based optimization algorithm with global convergence guarantees"
    - "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "influence diagrams for the optimization of a vehicle speed profile"):
  with members:
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "a method for using belief networks as influence diagrams"

Connected through hyperedge HE-5 (induced by source paper "bounded approximate symbolic dynamic programming for hybrid mdps"):
  with members:
    - "dynamic programming for structured continuous markov decision problems"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "a heuristic search approach to planning with continuous resources in stochastic domains"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-6 (induced by source paper "symbolic dynamic programming for discrete and continuous state mdps"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "dynamic programming for structured continuous markov decision problems"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "a heuristic search approach to planning with continuous resources in stochastic domains"
    - "decision theoretic planning structural assumptions and computational leverage"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-7 (induced by source paper "partitioned linear programming approximations for mdps"):
  with members:
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-8 (induced by source paper "a heuristic search approach to planning with continuous resources in stochastic domains"):
  with members:
    - "dynamic programming for structured continuous markov decision problems"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "decision theoretic planning with non markovian rewards"
    - "decision theoretic planning structural assumptions and computational leverage"

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)