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: "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding"
  Abstract: In this letter, with combined belief propagation (BP), mean field (MF), and expectation propagation (EP), an iterative receiver is designed for joint phase noise estimation, equalization, and decoding in a coded communication system. The presence of the phase noise results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (Soft-in EKS). In this letter, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the Soft-in EKS, leading to a significant performance improvement with similar complexity as demonstrated by simulation results.

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"
    - "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"
    - "expectation propagation for approximate bayesian inference"

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"
    - "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"
    - "message passing algorithms for channel estimation and decoding using approximate inference"

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)