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: "the compilation of decision models"
  Abstract: We introduce and analyze the problem of the compilation of decision models from a decision-theoretic perspective. The techniques described allow us to evaluate various configurations of compiled knowledge given the nature of evidential relationships in a domain, the utilities associated with alternative actions, the costs of run-time delays, and the costs of memory. We describe procedures for selecting a subset of the total observations available to be incorporated into a compiled situation-action mapping, in the context of a binary decision with conditional independence of evidence. The methods allow us to incrementally select the best pieces of evidence to add to the set of compiled knowledge in an engineering setting. After presenting several approaches to compilation, we exercise one of the methods to provide insight into the relationship between the distribution over weights of evidence and the preferred degree of compilation.

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"
    - "an empirical evaluation of a randomized algorithm for probabilistic inference"
    - "deliberation scheduling for time critical sequential decision making"
    - "two procedures for compiling influence diagrams"
    - "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"
    - "flexible policy construction by information refinement"
    - "two procedures for compiling influence diagrams"

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

Connected through hyperedge HE-4 (induced by source paper "symbolic probabilitistic inference in large bn2o networks"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "qualitative propagation and scenario based explanation of probabilistic reasoning"

Connected through hyperedge HE-5 (induced by source paper "reducing uncertainty in navigation and exploration"):
  with members:
    - "utility based control for computer vision"

Connected through hyperedge HE-6 (induced by source paper "two procedures for compiling influence diagrams"):
  with members:
    - "integrating probabilistic rules into neural networks a stochastic em learning algorithm"

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)