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: "evaluating influence diagrams using limids"
  Abstract: We present a new approach to the solution of decision problems formulated as influence diagrams. The approach converts the influence diagram into a simpler structure, the LImited Memory Influence Diagram (LIMID), where only the requisite information for the computation of optimal policies is depicted. Because the requisite information is explicitly represented in the diagram, the evaluation procedure can take advantage of it. In this paper we show how to convert an influence diagram to a LIMID and describe the procedure for finding an optimal strategy. Our approach can yield significant savings of memory and computational time when compared to traditional methods.

Connected through hyperedge HE-1 (induced by source paper "solving influence diagrams using hugin shafer shenoy and lazy propagation"):
  with members:
    - "from influence diagrams to junction trees"
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"
    - "nested junction trees"

Connected through hyperedge HE-2 (induced by source paper "temporal action graph games a new representation for dynamic games"):
  with members:
    - "computational analysis of perfect information position auctions"
    - "graphical models for game theory"
    - "context specific independence in bayesian networks"
    - "computing nash equilibria of action graph games"
    - "a continuation method for nash equilibria in structured games"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-3 (induced by source paper "exploiting model equivalences for solving interactive dynamic influence diagrams"):
  with members:
    - "graphical models for game theory"
    - "refinement and coarsening of bayesian networks"
    - "solving multistage influence diagrams using branch and bound search"
    - "knowledge combination in graphical multiagent model"
    - "networks of influence diagrams a formalism for representing agents beliefs and decision making processes"
    - "a framework for sequential planning in multi agent settings"

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)