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: "channel estimation and data equalization in frequency selective mimo systems with one bit quantization"
  Abstract: This paper addresses channel estimation and data equalization on frequency-selective 1-bit quantized Multiple Input-Multiple Output (MIMO) systems. No joint processing or Channel State Information is assumed at the transmitter, and therefore our findings are also applicable to the uplink of Multi-User MIMO systems. System models for both Orthogonal Division Frequency Multiplexing (OFDM) and single-carrier schemes are developed. A Cram\'er-Rao Lower Bound for the estimation problems is derived. The two nonlinear algorithms Expectation Maximization (EM) and Generalized Approximate Message Passing (GAMP) are adapted to the problems, and a linear method based on the Bussgang theorem is proposed. In the OFDM case, the linear method enables subcarrier-wise estimation, greatly reducing computational complexity. Simulations are carried out to compare the algorithms with different settings. The results turn out to be close to the Cram\'er-Rao bound in the low Signal to Noise Ratio (SNR) region. The OFDM setting is more suitable for the nonlinear algorithms, and that the linear methods incur a performance loss with respect to the nonlinear approaches. In the relevant low and medium SNR regions, the loss amounts to 2-3 dB and might well be justified in exchange for the reduced computational effort, especially in Massive MIMO settings.

Connected through hyperedge HE-1 (induced by source paper "reliable ofdm transmission with ultra low resolution adc"):
  with members:
    - "joint channel estimation decoding with frequency selective channels and few bit adcs"
    - "low complexity multiuser qam detection for uplink 1 bit massive mimo"
    - "a general framework for low resolution receivers for mimo channels"
    - "uplink multiuser massive mimo systems with low resolution adcs a coding theoretic approach"
    - "bayesian optimal data detector for mmwave ofdm system with low resolution adc"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "denoising based turbo compressed sensing"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"

Connected through hyperedge HE-2 (induced by source paper "bayesian optimal data detector for mmwave ofdm system with low resolution adc"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "mixed adc massive mimo"
    - "mixed adc massive mimo uplink in frequency selective channels"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"

Connected through hyperedge HE-3 (induced by source paper "narrowband channel estimation for hybrid beamforming millimeter wave communication systems with one bit quantization"):
  with members:
    - "wideband channel estimation for hybrid beamforming millimeter wave communication systems with low resolution adcs"
    - "adc bit allocation under a power constraint for mmwave massive mimo communication receivers"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "millimeter wave receiver efficiency a comprehensive comparison of beamforming schemes with low resolution adcs"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "expectation maximization gaussian mixture approximate message passing"

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)