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 algorithms for channel estimation and decoding using approximate inference"
  Abstract: We design iterative receiver schemes for a generic communication system by treating channel estimation and information decoding as an inference problem in graphical models. We introduce a recently proposed inference framework that combines belief propagation (BP) and the mean field (MF) approximation and includes these algorithms as special cases. We also show that the expectation propagation and expectation maximization (EM) algorithms can be embedded in the BP-MF framework with slight modifications. By applying the considered inference algorithms to our probabilistic model, we derive four different message-passing receiver schemes. Our numerical evaluation in a wireless scenario demonstrates that the receiver based on the BP-MF framework and its variant based on BP-EM yield the best compromise between performance, computational complexity and numerical stability among all candidate algorithms.

Connected through hyperedge HE-1 (induced by source paper "unifying message passing algorithms under the framework of constrained bethe free energy minimization"):
  with members:
    - "an iterative receiver for ofdm with sparsity based parametric channel estimation"
    - "hybrid vector perturbation precoding the blessing of approximate message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "message passing receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems"
    - "optimality of large mimo detection via approximate message passing"
    - "s amp approximate message passing for general matrix ensembles"

Connected through hyperedge HE-2 (induced by source paper "probabilistic receiver architecture combining bp mf and ep for multi signal detection"):
  with members:
    - "low complexity sparse bayesian learning using combined belief propagation and mean field with a stretched factor graph"
    - "a low complexity receiver using combined bp mf for joint channel estimation and decoding in ofdm systems"
    - "a low complexity ofdm receiver with combined gamp and mf message passing"
    - "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding"
    - "approximate joint map detection of co channel signals"
    - "message passing receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems"
    - "joint sparse channel estimation and decoding continuous and discrete domain sparsity"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-3 (induced by source paper "a low complexity ofdm receiver with combined gamp and mf message passing"):
  with members:
    - "joint sparse channel estimation and decoding continuous and discrete domain sparsity"
    - "expectation propagation for approximate bayesian inference"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "merging belief propagation and the mean field approximation a free energy approach"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-4 (induced by source paper "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding"):
  with members:
    - "calculation of the performance of communication systems from measured oscillator phase noise"
    - "merging belief propagation and the mean field approximation a free energy approach"
    - "a general computation rule for lossy summaries messages with examples from equalization"

Connected through hyperedge HE-5 (induced by source paper "message passing receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems"):
  with members:
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "bilinear generalized approximate message passing part i derivation"
    - "massive mimo with optimal power and training duration allocation"
    - "massive mimo for next generation wireless systems"
    - "kronecker product correlation model and limited feedback codebook design in a 3d channel model"
    - "variational free energies for compressed sensing"
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-6 (induced by source paper "joint sparse channel estimation and decoding continuous and discrete domain sparsity"):
  with members:
    - "variational bayesian inference of line spectra"
    - "atomic norm denoising with applications to line spectral estimation"
    - "fast and robust parametric estimation of jointly sparse channels"
    - "towards a mathematical theory of super resolution"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "merging belief propagation and the mean field approximation a free energy approach"
    - "compressive estimation of doubly selective channels in multicarrier systems leakage effects and sparsity enhancing processing"
    - "a message passing approach for joint channel estimation interference mitigation and decoding"

Connected through hyperedge HE-7 (induced by source paper "iterative receiver design with off the grid sparse channel estimation"):
  with members:
    - "atomic norm denoising with applications to line spectral estimation"
    - "towards a mathematical theory of super resolution"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "merging belief propagation and the mean field approximation a free energy approach"
    - "compressive estimation of doubly selective channels in multicarrier systems leakage effects and sparsity enhancing processing"
    - "a message passing approach for joint channel estimation interference mitigation and decoding"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-8 (induced by source paper "merging belief propagation and the mean field approximation a free energy approach"):
  with members:
    - "a new class of upper bounds on the log partition function"
    - "distributed iterative processing for interference channels with receiver cooperation"
    - "expectation maximization as message passing"

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)