human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "normal factor graphs as probabilistic models"):
  Abstract: We present a new probabilistic modelling framework based on the recent notion of normal factor graph (NFG). We show that the proposed NFG models and their transformations unify some existing models such as factor graphs, convolutional factor graphs, and cumulative distribution networks. The two subclasses of the NFG models, namely the constrained and generative models, exhibit a duality in their dependence structure. Transformation of NFG models further extends the power of this modelling framework. We point out the well-known NFG representations of parity and generator realizations of a linear code as generative and constrained models, and comment on a more prevailing duality in this context. Finally, we address the algorithmic aspect of computing the exterior function of NFGs and the inference problem on NFGs.

Includes members:
  Member "extending factor graphs so as to unify directed and undirected graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards completely lifted search based probabilistic inference")
  Member "a factor graph representation of probabilities in quantum mechanics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "double edge factor graphs definition properties and examples")
  Member "convolutional factor graphs as probabilistic models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non minimal triangulations for mixed stochastic deterministic graphical models")
  Member "cumulative distribution networks and the derivative sum product algorithm":
    also connected through other hyperedges:
      (none)
  Member "partition functions of normal factor graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "codes on graphs models for elementary algebraic topology and statistical physics")
  Member "normal factor graphs and holographic transformations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "codes on graphs models for elementary algebraic topology and statistical physics")
  Member "inference with multivariate heavy tails in linear models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heavy tailed analogues of the covariance matrix for ica")

Question: Please tell me which class the source paper 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.IT(Information Theory)