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: "spudd stochastic planning using decision diagrams"
  Abstract: Markov decisions processes (MDPs) are becoming increasing popular as models of decision theoretic planning. While traditional dynamic programming methods perform well for problems with small state spaces, structured methods are needed for large problems. We propose and examine a value iteration algorithm for MDPs that uses algebraic decision diagrams(ADDs) to represent value functions and policies. An MDP is represented using Bayesian networks and ADDs and dynamic programming is applied directly to these ADDs. We demonstrate our method on large MDPs (up to 63 million states) and show that significant gains can be had when compared to tree-structured representations (with up to a thirty-fold reduction in the number of nodes required to represent optimal value functions).

Connected through hyperedge HE-1 (induced by source paper "maximum causal entropy specification inference from demonstrations"):
  with members:
    - "a model counter s guide to probabilistic systems"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "learning task specifications from demonstrations"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "on directed information and gambling"

Connected through hyperedge HE-2 (induced by source paper "compiling stochastic constraint programs to and or decision diagrams"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "stochastic constraint programming"
    - "solving sat and bayesian inference with backtracking search"
    - "and or multi valued decision diagrams aomdds for graphical models"
    - "stochastic constraint programming a scenario based approach"

Connected through hyperedge HE-3 (induced by source paper "learning task specifications from demonstrations via the principle of maximum causal entropy"):
  with members:
    - "a model counter s guide to probabilistic systems"
    - "composable deep reinforcement learning for robotic manipulation"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-4 (induced by source paper "addmc exact weighted model counting with algebraic decision diagrams"):
  with members:
    - "solving sat and bayesian inference with backtracking search"
    - "probabilistic planning via heuristic forward search and weighted model counting"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-5 (induced by source paper "add lib decision diagrams in practice"):
  with members:
    - "chain reduction for binary and zero suppressed decision diagrams"

Connected through hyperedge HE-6 (induced by source paper "stochastic planning and lifted inference"):
  with members:
    - "the complexity of reasoning with fodd and gfodd"
    - "solving relational mdps with exogenous events and additive rewards"
    - "belief propagation for structured decision making"
    - "policy iteration for relational mdps"
    - "first order decision diagrams for relational mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-7 (induced by source paper "high dimensional stochastic optimal control using continuous tensor decompositions"):
  with members:
    - "tensorizing neural networks"
    - "playing atari with deep reinforcement learning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "the symbolic interior point method"):
  with members:
    - "recursive decomposition for nonconvex optimization"
    - "first order mixed integer linear programming"

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)