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: "inference using message propagation and topology transformation in vector gaussian continuous networks"
  Abstract: We extend Gaussian networks - directed acyclic graphs that encode probabilistic relationships between variables - to its vector form. Vector Gaussian continuous networks consist of composite nodes representing multivariates, that take continuous values. These vector or composite nodes can represent correlations between parents, as opposed to conventional univariate nodes. We derive rules for inference in these networks based on two methods: message propagation and topology transformation. These two approaches lead to the development of algorithms, that can be implemented in either a centralized or a decentralized manner. The domain of application of these networks are monitoring and estimation problems. This new representation along with the rules for inference developed here can be used to derive current Bayesian algorithms such as the Kalman filter, and provide a rich foundation to develop new algorithms. We illustrate this process by deriving the decentralized form of the Kalman filter. This work unifies concepts from artificial intelligence and modern control theory.

Connected through hyperedge HE-1 (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"
    - "nonuniform dynamic discretization in hybrid 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-2 (induced by source paper "mix nets factored mixtures of gaussians in bayesian networks with mixed continuous and discrete variables"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "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"
    - "models and selection criteria for regression and classification"

Connected through hyperedge HE-3 (induced by source paper "nonuniform dynamic discretization in hybrid networks"):
  with members:
    - "implementation of continuous bayesian networks using sums of weighted gaussians"
    - "state space abstraction for anytime evaluation of probabilistic networks"

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)