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: "tradeoffs in constructing and evaluating temporal influence diagrams"
  Abstract: This paper addresses the tradeoffs which need to be considered in reasoning using probabilistic network representations, such as Influence Diagrams (IDs). In particular, we examine the tradeoffs entailed in using Temporal Influence Diagrams (TIDs) which adequately capture the temporal evolution of a dynamic system without prohibitive data and computational requirements. Three approaches for TID construction which make different tradeoffs are examined: (1) tailoring the network at each time interval to the data available (rather then just copying the original Bayes Network for all time intervals); (2) modeling the evolution of a parsimonious subset of variables (rather than all variables); and (3) model selection approaches, which seek to minimize some measure of the predictive accuracy of the model without introducing too many parameters, which might cause "overfitting" of the model. Methods of evaluating the accuracy/efficiency of the tradeoffs are proposed.

Connected through hyperedge HE-1 (induced by source paper "constructing situation specific belief networks"):
  with members:
    - "pruning bayesian networks for efficient computation"
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "generating bayesian networks from probability logic knowledge bases"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "d separation from theorems to algorithms"
    - "integrating model construction and evaluation"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-2 (induced by source paper "tractable inference for complex stochastic processes"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "a scheme for approximating probabilistic inference"
    - "dynamic network models for forecasting"

Connected through hyperedge HE-3 (induced by source paper "knowledge engineering for large belief networks"):
  with members:
    - "a generalization of the noisy or model"
    - "practical issues in constructing a bayes belief network"

Connected through hyperedge HE-4 (induced by source paper "time critical dynamic decision making"):
  with members:
    - "reasoning about the value of decision model refinement methods and application"

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)