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 receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems"
  Abstract: In this paper, we address the message-passing receiver design for the 3D massive MIMO-OFDM systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. Specially, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response (CIR) between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization (EM)-based method is derived to learn the channel power-delay-profile (PDP). The proposed joint algorithm is assessed in 3D massive MIMO systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and 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"
    - "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"
    - "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"
    - "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding"
    - "approximate joint map detection of co channel signals"
    - "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)