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: "nonuniform dynamic discretization in hybrid networks"
  Abstract: We consider probabilistic inference in general hybrid networks, which include continuous and discrete variables in an arbitrary topology. We reexamine the question of variable discretization in a hybrid network aiming at minimizing the information loss induced by the discretization. We show that a nonuniform partition across all variables as opposed to uniform partition of each variable separately reduces the size of the data structures needed to represent a continuous function. We also provide a simple but efficient procedure for nonuniform partition. To represent a nonuniform discretization in the computer memory, we introduce a new data structure, which we call a Binary Split Partition (BSP) tree. We show that BSP trees can be an exponential factor smaller than the data structures in the standard uniform discretization in multiple dimensions and show how the BSP trees can be used in the standard join tree algorithm. We show that the accuracy of the inference process can be significantly improved by adjusting discretization with evidence. We construct an iterative anytime algorithm that gradually improves the quality of the discretization and the accuracy of the answer on a query. We provide empirical evidence that the algorithm converges.

Connected through hyperedge HE-1 (induced by source paper "region based approximation for high dimensional bayesian network models"):
  with members:
    - "performing bayesian risk aggregation using discrete approximation algorithms with graph factorization"
    - "loopy belief propagation for approximate inference an empirical study"
    - "generalized belief propagation on tree robust structured region graphs"
    - "on the choice of regions for generalized belief propagation"
    - "structured region graphs morphing ep into gbp"
    - "join graph propagation algorithms"

Connected through hyperedge HE-2 (induced by source paper "weighted positive binary decision diagrams for exact probabilistic inference"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "context specific independence in bayesian networks"
    - "using robdds for inference in bayesian networks with troubleshooting as an example"
    - "sum product networks a new deep architecture"
    - "counting belief propagation"
    - "and or multi valued decision diagrams aomdds for graphical models"

Connected through hyperedge HE-3 (induced by source paper "learning discrete bayesian networks from continuous data"):
  with members:
    - "a multivariate discretization method for learning bayesian networks from mixed data"
    - "minimum description length induction bayesianism and kolmogorov complexity"

Connected through hyperedge HE-4 (induced by source paper "a variational approximation for bayesian networks with discrete and continuous latent variables"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "inference using message propagation and topology transformation in vector gaussian continuous networks"
    - "optimal junction trees"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "global conditioning for probabilistic inference in belief networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-5 (induced by source paper "mix nets factored mixtures of gaussians in bayesian networks with mixed continuous and discrete variables"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian networks with local structure"
    - "a multivariate discretization method for learning bayesian networks from mixed data"
    - "the anchors hierachy using the triangle inequality to survive high dimensional data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "inference using message propagation and topology transformation in vector gaussian continuous networks"
    - "models and selection criteria for regression and classification"

Connected through hyperedge HE-6 (induced by source paper "exact inference in networks with discrete children of continuous parents"):
  with members:
    - "a variational approximation for bayesian networks with discrete and continuous latent variables"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "variational approximations between mean field theory and the junction tree algorithm"

Connected through hyperedge HE-7 (induced by source paper "inference in hybrid bayesian networks using mixtures of gaussians"):
  with members:
    - "mixtures of gaussians and minimum relative entropy techniques for modeling continuous uncertainties"
    - "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"

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)