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: "ideal reformulation of belief networks"
  Abstract: The intelligent reformulation or restructuring of a belief network can greatly increase the efficiency of inference. However, time expended for reformulation is not available for performing inference. Thus, under time pressure, there is a tradeoff between the time dedicated to reformulating the network and the time applied to the implementation of a solution. We investigate this partition of resources into time applied to reformulation and time used for inference. We shall describe first general principles for computing the ideal partition of resources under uncertainty. These principles have applicability to a wide variety of problems that can be divided into interdependent phases of problem solving. After, we shall present results of our empirical study of the problem of determining the ideal amount of time to devote to searching for clusters in belief networks. In this work, we acquired and made use of probability distributions that characterize (1) the performance of alternative heuristic search methods for reformulating a network instance into a set of cliques, and (2) the time for executing inference procedures on various belief networks. Given a preference model describing the value of a solution as a function of the delay required for its computation, the system selects an ideal time to devote to reformulation.

Connected through hyperedge HE-1 (induced by source paper "metareasoning for planning under uncertainty"):
  with members:
    - "selecting computations theory and applications"
    - "reasoning metareasoning and mathematical truth studies of theorem proving under limited resources"
    - "a bayesian approach to tackling hard computational problems"
    - "reasoning about beliefs and actions under computational resource constraints"

Connected through hyperedge HE-2 (induced by source paper "a graph theoretic analysis of information value"):
  with members:
    - "reasoning about the value of decision model refinement methods and application"
    - "reformulating inference problems through selective conditioning"
    - "efficient decision theoretic planning techniques and empirical analysis"
    - "dynamic construction of belief networks"
    - "chain graphs for learning"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "representing context sensitive knowledge in a network formalism a preliminary report"
    - "localized partial evaluation of belief networks"

Connected through hyperedge HE-3 (induced by source paper "state space abstraction for anytime evaluation of probabilistic networks"):
  with members:
    - "utility based abstraction and categorization"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "using tree decomposable structures to approximate belief networks"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "refinement and coarsening of bayesian networks"
    - "ideal a software package for analysis of influence diagrams"
    - "incremental probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "dynamic network updating techniques for diagnostic reasoning"):
  with members:
    - "a causal bayesian model for the diagnosis of appendicitis"
    - "model based influence diagrams for machine vision"
    - "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-5 (induced by source paper "a bayesian approach to tackling hard computational problems"):
  with members:
    - "reasoning metareasoning and mathematical truth studies of theorem proving under limited resources"
    - "a bayesian approach to learning bayesian networks with local structure"

Connected through hyperedge HE-6 (induced by source paper "tradeoffs in constructing and evaluating temporal influence diagrams"):
  with members:
    - "integrating model construction and evaluation"
    - "dynamic network models for forecasting"

Connected through hyperedge HE-7 (induced by source paper "time dependent utility and action under uncertainty"):
  with members:
    - "problem formulation as the reduction of a decision model"

Connected through hyperedge HE-8 (induced by source paper "reformulating inference problems through selective conditioning"):
  with members:
    - "stochastic simulation of bayesian belief networks"

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)