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: "convolutional factor graphs as probabilistic models"
  Abstract: Based on a recent development in the area of error control coding, we introduce the notion of convolutional factor graphs (CFGs) as a new class of probabilistic graphical models. In this context, the conventional factor graphs are referred to as multiplicative factor graphs (MFGs). This paper shows that CFGs are natural models for probability functions when summation of independent latent random variables is involved. In particular, CFGs capture a large class of linear models, where the linearity is in the sense that the observed variables are obtained as a linear ransformation of the latent variables taking arbitrary distributions. We use Gaussian models and independent factor models as examples to emonstrate the use of CFGs. The requirement of a linear transformation between latent variables (with certain independence restriction) and the bserved variables, to an extent, limits the modelling flexibility of CFGs. This structural restriction however provides a powerful analytic tool to the framework of CFGs; that is, upon taking the Fourier transform of the function represented by the CFG, the resulting function is represented by a FG with identical structure. This Fourier transform duality allows inference problems on a CFG to be solved on the corresponding dual MFG.

Connected through hyperedge HE-1 (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 decomposition of belief networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "normal factor graphs as probabilistic models"):
  with members:
    - "extending factor graphs so as to unify directed and undirected graphical models"
    - "a factor graph representation of probabilities in quantum mechanics"
    - "cumulative distribution networks and the derivative sum product algorithm"
    - "partition functions of normal factor graphs"
    - "normal factor graphs and holographic transformations"
    - "inference with multivariate heavy tails in linear models"

Connected through hyperedge HE-3 (induced by source paper "cumulative distribution networks and the derivative sum product algorithm"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "chain graphs for learning"

Connected through hyperedge HE-4 (induced by source paper "inference with multivariate heavy tails in linear models"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation theory and application"

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)