human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "channel estimation in broadband millimeter wave mimo systems with few bit adcs"):
  Abstract: We develop a broadband channel estimation algorithm for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters (ADCs). Our methodology exploits the joint sparsity of the mmWave MIMO channel in the angle and delay domains. We formulate the estimation problem as a noisy quantized compressed-sensing problem and solve it using efficient approximate message passing (AMP) algorithms. In particular, we model the angle-delay coefficients using a Bernoulli-Gaussian-mixture distribution with unknown parameters and use the expectation-maximization (EM) forms of the generalized AMP (GAMP) and vector AMP (VAMP) algorithms to simultaneously learn the distributional parameters and compute approximately minimum mean-squared error (MSE) estimates of the channel coefficients. We design a training sequence that allows fast, FFT-based implementation of these algorithms while minimizing peak-to-average power ratio at the transmitter, making our methods scale efficiently to large numbers of antenna elements and delays. We present the results of a detailed simulation study that compares our algorithms to several benchmarks. Our study investigates the effect of SNR, training length, training type, ADC resolution, and runtime on channel estimation MSE, mutual information, and achievable rate. It shows that our methods allow one-bit ADCs to perform comparably to infinite-bit ADCs at low SNR, and 4-bit ADCs to perform comparably to infinite-bit ADCs at medium SNR.

Includes members:
  Member "channel estimation and performance analysis of one bit massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance analysis of quantized uplink massive mimo ofdm with oversampling under adjacent channel interference")
  Member "uplink performance of wideband massive mimo with one bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "bayes optimal joint channel and data estimation for massive mimo with low precision adcs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "massive mimo with low resolution adcs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "achievable uplink rates for massive mimo with coarse quantization")
  Member "learning and free energies for vector approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "vector approximate message passing for the generalized linear model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "performance of the wideband massive uplink mimo with one bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "channel estimation and performance analysis of one bit massive mimo systems")
  Member "mixed adc massive mimo detectors performance analysis and design optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "performance analysis of quantized uplink massive mimo ofdm with oversampling under adjacent channel interference")

Question: Please tell me which class the source paper 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)