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 ofdm receiver with combined gamp and mf message passing"
  Abstract: With a unified belief propagation (BP) and mean field (MF) framework, we propose an iterative message passing receiver, which performs joint channel state and noise precision (the reciprocal of noise variance) estimation and decoding for OFDM systems. The recently developed generalized approximate message passing (GAMP) is incorporated to the BP-MF framework, where MF is used to handle observation factor nodes with unknown noise precision and GAMP is used for channel estimation in the time-frequency domain. Compared to state-of-the-art algorithms in the literature, the proposed algorithm either delivers similar performance with much lower complexity, or delivers much better performance with similar complexity. In addition, the proposed algorithm exhibits fastest convergence.

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 receiver using combined bp mf for joint channel estimation and decoding in ofdm systems"
    - "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)