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: "dynamic programming for structured continuous markov decision problems"
  Abstract: We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamically partitioned into regions where the value function is the same throughout the region. We first describe the algorithm for piecewise constant representations. We then extend it to piecewise linear representations, using techniques from POMDPs to represent and reason about linear surfaces efficiently. We show that for complex, structured problems, our approach exploits the natural structure so that optimal solutions can be computed efficiently.

Connected through hyperedge HE-1 (induced by source paper "learning state abstractions for transfer in continuous control"):
  with members:
    - "state representation learning for control an overview"
    - "value prediction network"
    - "near optimal behavior via approximate state abstraction"
    - "distral robust multitask reinforcement learning"
    - "learning to reinforcement learn"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "successor features for transfer in reinforcement learning"
    - "a vector contraction inequality for rademacher complexities"

Connected through hyperedge HE-2 (induced by source paper "learning world graphs to accelerate hierarchical reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "world discovery models"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "hierarchical policy learning is sensitive to goal space design"
    - "interpretable neural predictions with differentiable binary variables"
    - "model based reinforcement learning for atari"
    - "discovering options for exploration by minimizing cover time"
    - "keyin discovering subgoal structure with keyframe based video prediction"

Connected through hyperedge HE-3 (induced by source paper "bounded approximate symbolic dynamic programming for hybrid mdps"):
  with members:
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "a heuristic search approach to planning with continuous resources in stochastic domains"
    - "solving factored mdps with hybrid state and action variables"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-4 (induced by source paper "probabilistic planning for continuous dynamic systems under bounded risk"):
  with members:
    - "colin planning with continuous linear numeric change"
    - "modelling mixed discrete continuous domains for planning"
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-5 (induced by source paper "symbolic dynamic programming for discrete and continuous state mdps"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "a heuristic search approach to planning with continuous resources in stochastic domains"
    - "solving factored mdps with hybrid state and action variables"
    - "decision theoretic planning structural assumptions and computational leverage"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-6 (induced by source paper "alarms alerting and reasoning management system for next generation aircraft hazards"):
  with members:
    - "towards adjustable autonomy for the real world"

Connected through hyperedge HE-7 (induced by source paper "a heuristic search approach to planning with continuous resources in stochastic domains"):
  with members:
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "decision theoretic planning with non markovian rewards"
    - "solving factored mdps with hybrid state and action variables"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-8 (induced by source paper "solving factored mdps with hybrid state and action variables"):
  with members:
    - "from influence diagrams to junction trees"
    - "approximating map using local search"
    - "metrics for finite markov decision processes"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "approximate linear programming for first order mdps"
    - "solving map exactly using systematic search"
    - "metrics for markov decision processes with infinite state spaces"
    - "solving factored mdps with continuous and discrete variables"

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)