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: "model based influence diagrams for machine vision"
  Abstract: We show an approach to automated control of machine vision systems based on incremental creation and evaluation of a particular family of influence diagrams that represent hypotheses of imagery interpretation and possible subsequent processing decisions. In our approach, model-based machine vision techniques are integrated with hierarchical Bayesian inference to provide a framework for representing and matching instances of objects and relationships in imagery and for accruing probabilities to rank order conflicting scene interpretations. We extend a result of Tatman and Shachter to show that the sequence of processing decisions derived from evaluating the diagrams at each stage is the same as the sequence that would have been derived by evaluating the final influence diagram that contains all random variables created during the run of the vision system.

Connected through hyperedge HE-1 (induced by source paper "network fragments representing knowledge for constructing probabilistic models"):
  with members:
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "causal independence for knowledge acquisition and inference"
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "object oriented bayesian networks"
    - "utility based control for computer vision"

Connected through hyperedge HE-2 (induced by source paper "dynamic network updating techniques for diagnostic reasoning"):
  with members:
    - "a causal bayesian model for the diagnosis of appendicitis"
    - "ideal reformulation of belief networks"
    - "problem formulation as the reduction of a decision model"
    - "refinement and coarsening of bayesian networks"
    - "ideal a software package for analysis of influence diagrams"
    - "dynamic construction of belief networks"

Connected through hyperedge HE-3 (induced by source paper "incremental dynamic construction of layered polytree networks"):
  with members:
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "stochastic simulation of bayesian belief networks"

Connected through hyperedge HE-4 (induced by source paper "efficient enumeration of instantiations in bayesian networks"):
  with members:
    - "an efficient approach for finding the mpe in belief networks"
    - "ideal a software package for analysis of influence diagrams"

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.CV(Computer Vision and Pattern Recognition)