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: "non linear transformations of gaussians and gaussian mixtures with implications on estimation and information theory"
  Abstract: This paper investigates the statistical properties of non- linear trasformations (NLT) of random variables, in order to establish useful tools for estimation and information theory. Specifically, the paper focuses on linear regression analysis of the NLT output and derives sufficient general conditions to establish when the input-output regression coefficient is e qual to the partial regression coefficient of the output with respect to a (additive) part of the input. A speci al case is represented by zero-mean Gaussian inputs, obtained as the sum of other zero-mean Gaussian random variables. The paper shows how this property can be generalized to the regression coefficient of non-linear transformations of Gaussianmixtures. Due to its generality, and the wide use of Gaussians and Gaussian-mixtures to statistically model several phenomena, this theoretical framework can fin d applications in multiple disciplines, such as communication, estimation, and information theory, when part of the nonlinear transformation input is the quantity of interest and the other part is the noise. In particular, the paper shows how the said properties can be exploited to simplify closed-form computation of the signal-to-noise ratio (SNR), the estimation mean-squared error (MSE), and bounds on the mutual information in additive non-Gaussian (possibly non-linear) channels, also establishing relati onships among them.

Connected through hyperedge HE-1 (induced by source paper "unveiling bias compensation in turbo based algorithms for discrete compressed sensing"):
  with members:
    - "low complexity iterative algorithms for discrete compressed sensing"
    - "algorithms for the iterative estimation of discrete valued sparse vectors"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "bayesian estimation of a gaussian source in middleton s class a impulsive noise"):
  with members:
    - "mutual information and minimum mean square error in gaussian channels"

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.IT(Information Theory)