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: "mixtures of gaussians and minimum relative entropy techniques for modeling continuous uncertainties"
  Abstract: Problems of probabilistic inference and decision making under uncertainty commonly involve continuous random variables. Often these are discretized to a few points, to simplify assessments and computations. An alternative approximation is to fit analytically tractable continuous probability distributions. This approach has potential simplicity and accuracy advantages, especially if variables can be transformed first. This paper shows how a minimum relative entropy criterion can drive both transformation and fitting, illustrating with a power and logarithm family of transformations and mixtures of Gaussian (normal) distributions, which allow use of efficient influence diagram methods. The fitting procedure in this case is the well-known EM algorithm. The selection of the number of components in a fitted mixture distribution is automated with an objective that trades off accuracy and computational cost.

Connected through hyperedge HE-1 (induced by source paper "laplace s method approximations for probabilistic inference in belief networks with continuous variables"):
  with members:
    - "a linear approximation method for probabilistic inference"

Connected through hyperedge HE-2 (induced by source paper "probabilistic constraint satisfaction with non gaussian noise"):
  with members:
    - "a probabilistic algorithm for calculating structure borrowing from simulated annealing"

Connected through hyperedge HE-3 (induced by source paper "inference in hybrid bayesian networks using mixtures of gaussians"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "a variational approximation for bayesian networks with discrete and continuous latent variables"
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "exact inference in networks with discrete children of continuous parents"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "hybrid bayesian networks with linear deterministic variables"

Connected through hyperedge HE-4 (induced by source paper "hybrid influence diagrams using mixtures of truncated exponentials"):
  with members:
    - "solving influence diagrams using hugin shafer shenoy and lazy propagation"
    - "from influence diagrams to junction trees"
    - "lazy evaluation of symmetric bayesian decision problems"
    - "using potential influence diagrams for probabilistic inference and decision making"

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)