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: "hybrid vector perturbation precoding the blessing of approximate message passing"
  Abstract: Vector perturbation (VP) precoding is a promising technique for multiuser communication systems operating in the downlink. In this work, we introduce a hybrid framework to improve the performance of lattice reduction (LR) aided (LRA) VP. Firstly, we perform a simple precoding using zero forcing (ZF) or successive interference cancellation (SIC) based on a reduced lattice basis. Since the signal space after LR-ZF or LR-SIC precoding can be shown to be bounded to a small range, then along with sufficient orthogonality of the lattice basis guaranteed by LR, they collectively pave the way for the subsequent application of an approximate message passing (AMP) algorithm, which further boosts the performance of any suboptimal precoder. Our work shows that the AMP algorithm in compressed sensing can be beneficial for a lattice decoding problem whose signal constraint lies in \mathbb{Z} and entries of the input lattice basis not necessarily being i.i.d. Gaussian. Numerical results show that the developed hybrid scheme can provide performance enhancement with negligible increase in the complexity.

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"
    - "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"
    - "expectation propagation for approximate bayesian 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)