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: "optimal decomposition of belief networks"
  Abstract: In this paper, optimum decomposition of belief networks is discussed. Some methods of decomposition are examined and a new method - the method of Minimum Total Number of States (MTNS) - is proposed. The problem of optimum belief network decomposition under our framework, as under all the other frameworks, is shown to be NP-hard. According to the computational complexity analysis, an algorithm of belief network decomposition is proposed in (Wee, 1990a) based on simulated annealing.

Connected through hyperedge HE-1 (induced by source paper "potential maximal cliques parameterized by edge clique cover"):
  with members:
    - "hyperbench a benchmark and tool for hypergraphs and empirical findings"
    - "constraint solving via fractional edge covers"
    - "on the tractability of optimization problems on h graphs"
    - "ranked enumeration of minimal triangulations"
    - "positive instance driven dynamic programming for treewidth"
    - "on distance d independent set and other problems in graphs with few minimal separators"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "on the number of minimal separators in graphs"

Connected through hyperedge HE-2 (induced by source paper "proving the np completeness of optimal moral graph triangulation"):
  with members:
    - "efficient approximation for triangulation of minimum treewidth"

Connected through hyperedge HE-3 (induced by source paper "efficient approximation for triangulation of minimum treewidth"):
  with members:
    - "knowledge engineering for large belief networks"
    - "object oriented bayesian networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"

Connected through hyperedge HE-4 (induced by source paper "a computational scheme for reasoning in dynamic probabilistic networks"):
  with members:
    - "sensor validation using dynamic belief networks"
    - "dynamic network models for forecasting"

Connected through hyperedge HE-5 (induced by source paper "from relational databases to belief networks"):
  with members:
    - "bayesian assessment of a connectionist model for fault detection"

Connected through hyperedge HE-6 (induced by source paper "non minimal triangulations for mixed stochastic deterministic graphical models"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "optimal junction trees"
    - "pre processing for triangulation of probabilistic networks"
    - "bucket elimination a unifying framework for several probabilistic inference"
    - "hybrid processing of beliefs and constraints"
    - "on triangulating dynamic graphical models"
    - "value elimination bayesian inference via backtracking search"
    - "convolutional factor graphs as probabilistic models"

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)