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: "message passing decoding of lattices using gaussian mixtures"
  Abstract: A lattice decoder which represents messages explicitly as a mixture of Gaussians functions is given. In order to prevent the number of functions in a mixture from growing as the decoder iterations progress, a method for replacing N Gaussian functions with M Gaussian functions, with M < N, is given. A squared distance metric is used to select functions for combining. A pair of selected Gaussians is replaced by a single Gaussian with the same first and second moments. The metric can be computed efficiently, and at the same time, the proposed algorithm empirically gives good results, for example, a dimension 100 lattice has a loss of 0.2 dB in signal-to-noise ratio at a probability of symbol error of 10^{-5}.

Connected through hyperedge HE-1 (induced by source paper "fast decoding of low density lattice codes"):
  with members:
    - "ldpc lattice codes for full duplex relay channels"
    - "low density lattice codes"

Connected through hyperedge HE-2 (induced by source paper "construction pi_a and pi_d lattices construction goodness and decoding algorithms"):
  with members:
    - "lda lattices without dithering achieve capacity on the gaussian channel"
    - "polar lattices for lossy compression"
    - "construction of capacity achieving lattice codes polar lattices"
    - "multistage compute and forward with multilevel lattice codes based on product constructions"
    - "lattices from codes for harnessing interference an overview and generalizations"
    - "lattices over eisenstein integers for compute and forward"
    - "connections between construction d and related constructions of lattices"
    - "a simple proof for the existence of good pairs of nested lattices"

Connected through hyperedge HE-3 (induced by source paper "fault identification via non parametric belief propagation"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "distributed large scale network utility maximization"
    - "a low density lattice decoder via non parametric belief propagation"
    - "gaussian belief propagation based multiuser detection"
    - "iterative hard thresholding for compressed sensing"
    - "bayesian compressive sensing via belief propagation"
    - "low density lattice codes"
    - "belief propagation based multi user detection"

Connected through hyperedge HE-4 (induced by source paper "a low density lattice decoder via non parametric belief propagation"):
  with members:
    - "fixing convergence of gaussian belief propagation"
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation theory and application"
    - "gaussian belief propagation solver for systems of linear equations"
    - "bayesian compressive sensing via belief propagation"
    - "low density lattice codes"

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)