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: "fixing convergence of gaussian belief propagation"
  Abstract: Gaussian belief propagation (GaBP) is an iterative message-passing algorithm for inference in Gaussian graphical models. It is known that when GaBP converges it converges to the correct MAP estimate of the Gaussian random vector and simple sufficient conditions for its convergence have been established. In this paper we develop a double-loop algorithm for forcing convergence of GaBP. Our method computes the correct MAP estimate even in cases where standard GaBP would not have converged. We further extend this construction to compute least-squares solutions of over-constrained linear systems. We believe that our construction has numerous applications, since the GaBP algorithm is linked to solution of linear systems of equations, which is a fundamental problem in computer science and engineering. As a case study, we discuss the linear detection problem. We show that using our new construction, we are able to force convergence of Montanari's linear detection algorithm, in cases where it would originally fail. As a consequence, we are able to increase significantly the number of users that can transmit concurrently.

Connected through hyperedge HE-1 (induced by source paper "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"):
  with members:
    - "low complexity near optimal signal detection for uplink large scale mimo systems"
    - "large system analysis of cognitive radio network via partially projected regularized zero forcing precoding"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy efficient sensing and communication of parallel gaussian sources"
    - "par aware large scale multi user mimo ofdm downlink"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "gaussian belief propagation theory and application"

Connected through hyperedge HE-2 (induced by source paper "compressive hyperspectral imaging via approximate message passing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive hyperspectral imaging with side information"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "approximate message passing in coded aperture snapshot spectral imaging"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive hyperspectral imaging with side information"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "on the convergence of approximate message passing with arbitrary matrices"):
  with members:
    - "learning and free energies for vector approximate message passing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"
    - "variational free energies for compressed sensing"
    - "on convergence of approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"

Connected through hyperedge HE-5 (induced by source paper "message passing algorithms for quadratic minimization"):
  with members:
    - "convergent message passing algorithms a unifying view"
    - "norm product belief propagation primal dual message passing for approximate inference"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"

Connected through hyperedge HE-6 (induced by source paper "properties of bethe free energies and message passing in gaussian models"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "bounds on the bethe free energy for gaussian networks"
    - "graph zeta function in the bethe free energy and loopy belief propagation"
    - "gaussian belief propagation theory and application"

Connected through hyperedge HE-7 (induced by source paper "a low density lattice decoder via non parametric belief propagation"):
  with members:
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation theory and application"
    - "gaussian belief propagation solver for systems of linear equations"
    - "message passing decoding of lattices using gaussian mixtures"
    - "bayesian compressive sensing via belief propagation"
    - "low density lattice codes"

Connected through hyperedge HE-8 (induced by source paper "distributed large scale network utility maximization"):
  with members:
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation solver for systems of linear equations"
    - "polynomial linear programming with gaussian belief propagation"

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)