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 low complexity receiver using combined bp mf for joint channel estimation and decoding in ofdm systems"
  Abstract: We proposed an iterative message passing receiver, which jointly performs channel state information and noise precision estimation and decoding, for OFDM systems. A message passing framework, which combines belief propagation (BP) and mean field (MF), is exploited on a stretched factor graph, where MF message update rule is used for observation factor nodes, so that noise precision (the reciprocal of AWGN variable) can be estimated. We introduce low complexity generalized approximate message passing (GAMP) method into the combined BP-MF framework to carry out channel estimation in time-frequency domain. Our numerical assessment demonstrates that the proposed receiver yields the same performance with the state of art BP-MF based algorithm while have much lower computational complexity.

Connected through hyperedge HE-1 (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 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"
    - "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)