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: "two procedures for compiling influence diagrams"
  Abstract: Two algorithms are presented for "compiling" influence diagrams into a set of simple decision rules. These decision rules define simple-to-execute, complete, consistent, and near-optimal decision procedures. These compilation algorithms can be used to derive decision procedures for human teams solving time constrained decision problems.

Connected through hyperedge HE-1 (induced by source paper "flexible policy construction by information refinement"):
  with members:
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "some properties of joint probability distributions"
    - "the compilation of decision models"
    - "an empirical evaluation of a randomized algorithm for probabilistic inference"
    - "deliberation scheduling for time critical sequential decision making"
    - "exploiting causal independence in bayesian network inference"
    - "provably bounded optimal agents"

Connected through hyperedge HE-2 (induced by source paper "an anytime algorithm for decision making under uncertainty"):
  with members:
    - "decision making using probabilistic inference methods"
    - "utility based abstraction and categorization"
    - "reasoning about the value of decision model refinement methods and application"
    - "some properties of joint probability distributions"
    - "the compilation of decision models"
    - "flexible policy construction by information refinement"

Connected through hyperedge HE-3 (induced by source paper "estimating the value of computation in flexible information refinement"):
  with members:
    - "the compilation of decision models"
    - "an anytime algorithm for decision making under uncertainty"
    - "flexible policy construction by information refinement"
    - "exploiting causal independence in bayesian network inference"

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)