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: "bayesian optimal data detector for hybrid mmwave mimo ofdm systems with low resolution adcs"
  Abstract: Hybrid analog–digital precoding architectures and low-resolution analog-to-digital converter (ADC) receivers are two solutions to reduce hardware cost and power consumption for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication systems with large antenna arrays. In this study, we consider a mmWave MIMO-orthogonal frequency division multiplexing (OFDM) receiver with a generalized hybrid architecture in which a small number of radio frequency (RF) chains and low-resolution ADCs are employed simultaneously. Owing to the strong nonlinearity introduced by low-resolution ADCs, the task of data detection is challenging, particularly achieving a Bayesian optimal data detection. This study aims to fill this gap. By using a generalized expectation consistent signal recovery technique, we propose a computationally efficient data detection algorithm that provides a minimum mean-square error estimate on data symbols and is extended to a mixed-ADC architecture. Considering particular structure of MIMO-OFDM channel matrix, we provide a low-complexity realization in which only fast fourier transform (FFT) operation and matrix-vector multiplications are required. Furthermore, we present an analytical framework to study the theoretical performance of the detector in the large-system limit, which can precisely evaluate the performance expressions, such as mean-square error and symbol error rate. Based on this optimal detector, the potential of adding a few low-resolution RF chains and high-resolution ADCs for a mixed-ADC architecture is investigated. Simulation results confirm the accuracy of our theoretical analysis and can be used for system design rapidly. The results reveal that adding a few low-resolution RF chains to original unquantized systems can obtain significant gains.

Connected through hyperedge HE-1 (induced by source paper "soft output detection methods for sparse millimeter wave mimo systems with low precision adcs"):
  with members:
    - "joint channel estimation decoding with frequency selective channels and few bit adcs"
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "one bit sphere decoding for uplink massive mimo systems with one bit adcs"
    - "performance analysis of mixed adc massive mimo systems over rician fading channels"
    - "bayesian optimal data detector for mmwave ofdm system with low resolution adc"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "on the spectral efficiency of massive mimo systems with low resolution adcs"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"

Connected through hyperedge HE-2 (induced by source paper "hardware constrained millimeter wave systems for 5g challenges opportunities and solutions"):
  with members:
    - "a low resolution adc module assisted hybrid beamforming architecture for mmwave communications"
    - "small scale local area and transitional millimeter wave propagation for 5g communications"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

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)