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: "mixed adc massive mimo"
  Abstract: Motivated by the demand for energy-efficient communication solutions in the next generation cellular network, a mixed-ADC architecture for massive multiple-input-multiple-output (MIMO) systems is proposed, which differs from previous works in that herein one-bit analog-to-digital converters (ADCs) partially replace the conventionally assumed high-resolution ADCs. The information-theoretic tool of generalized mutual information (GMI) is exploited to analyze the achievable data rates of the proposed system architecture and an array of analytical results of engineering interest are obtained. For fixed single-input-multiple-output (SIMO) channels, a closed-form expression of the GMI is derived, based on which the linear combiner is optimized. The analysis is then extended to ergodic fading channels, for which tight lower and upper bounds of the GMI are obtained. Impacts of dithering and imperfect channel state information (CSI) are also investigated, and it is shown that dithering can remarkably improve the system performance while imperfect CSI only introduces a marginal rate loss. Finally, the analytical framework is applied to the multiuser access scenario. Numerical results demonstrate that the mixed-ADC architecture with a relatively small number of high-resolution ADCs is able to achieve a large fraction of the channel capacity of conventional architecture, while reduce the energy consumption considerably even compared with antenna selection, for both single-user and multiuser scenarios.

Connected through hyperedge HE-1 (induced by source paper "a regression approach to certain information transmission problems"):
  with members:
    - "learning to detect"
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "one bit ofdm receivers via deep learning"
    - "deepcode feedback codes via deep learning"
    - "learning the mmse channel estimator"
    - "a model driven deep learning network for mimo detection"
    - "a two step learning and interpolation method for location based channel database"

Connected through hyperedge HE-2 (induced by source paper "deep learning based channel estimation for massive mimo with mixed resolution adcs"):
  with members:
    - "hybrid precoding based millimeter wave massive mimo noma with simultaneous wireless information and power transfer"
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "deep learning in physical layer communications"
    - "mixed adc dac multipair massive mimo relaying systems performance analysis and power optimization"
    - "performance analysis of mixed adc massive mimo systems over rician fading channels"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "uplink achievable rate for massive mimo systems with low resolution adc"

Connected through hyperedge HE-3 (induced by source paper "discussions on signal uncertainty principle in shannon channel capacity equation and research on breaking shannon limit method"):
  with members:
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "index modulation techniques for 5g wireless networks"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "energy efficiency in massive mimo based 5g networks opportunities and challenges"

Connected through hyperedge HE-4 (induced by source paper "bayesian optimal data detector for hybrid mmwave mimo ofdm systems with low resolution adcs"):
  with members:
    - "resolution adaptive hybrid mimo architectures for millimeter wave communications"
    - "performance analysis of mixed adc massive mimo systems over rician fading channels"
    - "bayesian optimal data detector for mmwave ofdm system with low resolution adc"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "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 uplink in frequency selective channels"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-5 (induced by source paper "spectral efficiency of mixed adc massive mimo"):
  with members:
    - "a comparison of hybrid beamforming and digital beamforming with low resolution adcs for multiple users and imperfect csi"
    - "spatial characteristics of distortion radiated from antenna arrays with transceiver nonlinearities"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "impact of spatial filtering on distortion from low noise amplifiers in massive mimo base stations"
    - "performance analysis of mixed adc massive mimo systems over rician fading channels"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "mixed adc massive mimo uplink in frequency selective channels"

Connected through hyperedge HE-6 (induced by source paper "mixed adc dac multipair massive mimo relaying systems performance analysis and power optimization"):
  with members:
    - "all technologies work together for good a glance to future mobile networks"
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "spectral efficiency of mixed adc massive mimo"
    - "multipair massive mimo relaying systems with one bit adc s and dac s"
    - "on performance of quantized transceiver in multiuser massive mimo downlinks"
    - "the distributed mimo scenario can ideal adcs be replaced by low resolution adcs"
    - "spectral efficiency of multipair massive mimo two way relaying with hardware impairments"
    - "channel estimation and performance analysis of one bit massive mimo systems"

Connected through hyperedge HE-7 (induced by source paper "one bit ofdm receivers via deep learning"):
  with members:
    - "ofdm autoencoder for end to end learning of communications systems"
    - "an introduction to deep learning for the physical layer"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "bayesian optimal data detector for mmwave ofdm system with low resolution adc"
    - "detection algorithms for communication systems using deep learning"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "uplink performance of wideband massive mimo with one bit adcs"

Connected through hyperedge HE-8 (induced by source paper "on the uplink achievable rate for massive mimo with 1 bit adc and superimposed pilots"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo"
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"

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)