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: "a method for using belief networks as influence diagrams"
  Abstract: This paper demonstrates a method for using belief-network algorithms to solve influence diagram problems. In particular, both exact and approximation belief-network algorithms may be applied to solve influence-diagram problems. More generally, knowing the relationship between belief-network and influence-diagram problems may be useful in the design and development of more efficient influence diagram algorithms.

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

Connected through hyperedge HE-2 (induced by source paper "lazy evaluation of symmetric bayesian decision problems"):
  with members:
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "d separation from theorems to algorithms"
    - "lazy propagation in junction trees"

Connected through hyperedge HE-3 (induced by source paper "bart a bayesian reasoning tool for knowledge based systems"):
  with members:
    - "selecting uncertainty calculi and granularity an experiment in trading off precision and complexity"
    - "david influence diagram processing system for the macintosh"
    - "decision theoretic control of problem solving principles and architecture"
    - "knet integrating hypermedia and bayesian modeling"

Connected through hyperedge HE-4 (induced by source paper "probabilistic inference in influence diagrams"):
  with members:
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-5 (induced by source paper "evaluating influence diagrams using limids"):
  with members:
    - "decision making using probabilistic inference methods"
    - "bayes ball the rational pastime for determining irrelevance and requisite information in belief networks and influence diagrams"
    - "from influence diagrams to junction trees"

Connected through hyperedge HE-6 (induced by source paper "decision making using probabilistic inference methods"):
  with members:
    - "a fusion algorithm for solving bayesian decision problems"
    - "evidence absorption and propagation through evidence reversals"

Connected through hyperedge HE-7 (induced by source paper "knowledge based decision model construction for hierarchical diagnosis a preliminary report"):
  with members:
    - "decision making using probabilistic inference methods"

Connected through hyperedge HE-8 (induced by source paper "a sensitivity analysis of pathfinder"):
  with members:
    - "knet integrating hypermedia and bayesian modeling"

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)