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: "decision making using probabilistic inference methods"
  Abstract: The analysis of decision making under uncertainty is closely related to the analysis of probabilistic inference. Indeed, much of the research into efficient methods for probabilistic inference in expert systems has been motivated by the fundamental normative arguments of decision theory. In this paper we show how the developments underlying those efficient methods can be applied immediately to decision problems. In addition to general approaches which need know nothing about the actual probabilistic inference method, we suggest some simple modifications to the clustering family of algorithms in order to efficiently incorporate decision making capabilities.

Connected through hyperedge HE-1 (induced by source paper "influence diagrams for the optimization of a vehicle speed profile"):
  with members:
    - "from influence diagrams to junction trees"
    - "a method for using belief networks as influence diagrams"
    - "solving factored mdps with hybrid state and action variables"

Connected through hyperedge HE-2 (induced by source paper "flexible policy construction by information refinement"):
  with members:
    - "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"
    - "two procedures for compiling influence diagrams"
    - "exploiting causal independence in bayesian network inference"
    - "provably bounded optimal agents"

Connected through hyperedge HE-3 (induced by source paper "solving limited memory influence diagrams using branch and bound search"):
  with members:
    - "solving influence diagrams using hugin shafer shenoy and lazy propagation"
    - "from influence diagrams to junction trees"
    - "an anytime algorithm for decision making under uncertainty"
    - "efficient value of information computation"
    - "solving multistage influence diagrams using branch and bound search"
    - "strategy selection in influence diagrams using imprecise probabilities"
    - "solving limited memory influence diagrams"

Connected through hyperedge HE-4 (induced by source paper "an anytime algorithm for decision making under uncertainty"):
  with members:
    - "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"
    - "two procedures for compiling influence diagrams"

Connected through hyperedge HE-5 (induced by source paper "myopic value of information in influence diagrams"):
  with members:
    - "value of evidence on influence diagrams"
    - "from influence diagrams to junction trees"
    - "a graph theoretic analysis of information value"
    - "using potential influence diagrams for probabilistic inference and decision making"

Connected through hyperedge HE-6 (induced by source paper "lazy evaluation of symmetric bayesian decision problems"):
  with members:
    - "from influence diagrams to junction trees"
    - "d separation from theorems to algorithms"
    - "lazy propagation in junction trees"
    - "a method for using belief networks as influence diagrams"

Connected through hyperedge HE-7 (induced by source paper "using potential influence diagrams for probabilistic inference and decision making"):
  with members:
    - "a fusion algorithm for solving bayesian decision problems"
    - "directed reduction algorithms and decomposable graphs"
    - "d separation from theorems to algorithms"
    - "evidence absorption and propagation through evidence reversals"

Connected through hyperedge HE-8 (induced by source paper "probabilistic inference in influence diagrams"):
  with members:
    - "from influence diagrams to junction trees"
    - "a method for using belief networks as influence diagrams"
    - "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)