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: "quantized massive mu mimo ofdm uplink"
  Abstract: Coarse quantization at the base station (BS) of a massive multi-user (MU) multiple-input multiple-output (MIMO) wireless system promises significant power and cost savings. Coarse quantization also enables significant reductions of the raw analog-to-digital converter (ADC) data that must be transferred from a spatially-separated antenna array to the baseband processing unit. The theoretical limits as well as practical transceiver algorithms for such quantized MU-MIMO systems operating over frequency-flat, narrowband channels have been studied extensively. However, the practically relevant scenario where such communication systems operate over frequency-selective, wideband channels is less well understood. This paper investigates the uplink performance of a quantized massive MU-MIMO system that deploys orthogonal frequency-division multiplexing (OFDM) for wideband communication. We propose new algorithms for quantized maximum a-posteriori (MAP) channel estimation and data detection, and we study the associated performance/quantization trade-offs. Our results demonstrate that coarse quantization (e.g., four to six bits, depending on the ratio between the number of BS antennas and the number of users) in massive MU-MIMO-OFDM systems entails virtually no performance loss compared to the infinite-precision case at no additional cost in terms of baseband processing complexity.

Connected through hyperedge HE-1 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints"):
  with members:
    - "tradeoff between delay and high snr capacity in quantized mimo systems"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "on mimo channel capacity with output quantization constraints"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "a general framework for low resolution receivers for mimo channels"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "generalized expectation consistent signal recovery for nonlinear measurements"

Connected through hyperedge HE-2 (induced by source paper "performance analysis of quantized uplink massive mimo ofdm with oversampling under adjacent channel interference"):
  with members:
    - "massive mu mimo ofdm uplink with hardware impairments modeling and analysis"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "linear precoding with low resolution dacs for massive mu mimo ofdm downlink"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "massive mimo ten myths and one critical question"
    - "one bit massive mimo channel estimation and high order modulations"

Connected through hyperedge HE-3 (induced by source paper "hardware distortion correlation has negligible impact on ul massive mimo spectral efficiency"):
  with members:
    - "massive mimo has unlimited capacity"
    - "can hardware distortion correlation be neglected when analyzing uplink se in massive mimo"
    - "out of band radiation from antenna arrays clarified"
    - "spatial characteristics of distortion radiated from antenna arrays with transceiver nonlinearities"
    - "performance characterization of a real time massive mimo system with los mobile channels"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "achievable uplink rates for massive mimo with coarse quantization"
    - "impact of spatial filtering on distortion from low noise amplifiers in massive mimo base stations"

Connected through hyperedge HE-4 (induced by source paper "massive mu mimo ofdm uplink with direct rf sampling and 1 bit adcs"):
  with members:
    - "timing and frequency synchronization for 1 bit massive mu mimo ofdm downlink"
    - "massive mu mimo ofdm uplink with hardware impairments modeling and analysis"
    - "one bit massive mimo precoding via a minimum symbol error probability design"
    - "directional frame timing synchronization in wideband millimeter wave systems with low resolution adcs"
    - "one bit sphere decoding for uplink massive mimo systems with one bit adcs"
    - "performance analysis for time of arrival estimation with oversampled low complexity 1 bit a d conversion"
    - "quantized precoding for multi antenna downlink channels with magiq"
    - "nonlinear precoding for phase quantized constant envelope massive mu mimo ofdm"

Connected through hyperedge HE-5 (induced by source paper "autoencoder based error correction coding for one bit quantization"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "learning a code machine learning for approximate non linear coded computation"
    - "deepcode feedback codes via deep learning"
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-6 (induced by source paper "investigation of channel estimation techniques with 1 bit quantization and oversampling for multiple antenna systems"):
  with members:
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "performance analysis for time of arrival estimation with oversampled low complexity 1 bit a d conversion"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "a lower bound for the fisher information measure"

Connected through hyperedge HE-7 (induced by source paper "supervised learning for multi hop mu mimo communications with one bit transceivers"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "multipair massive mimo relaying systems with one bit adc s and dac s"
    - "one bit sphere decoding for uplink massive mimo systems with one bit adcs"
    - "an introduction to deep learning for the physical layer"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "detection algorithms for communication systems using deep learning"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-8 (induced by source paper "on multiterminal communication over mimo channels with one bit adcs at the receivers"):
  with members:
    - "tradeoff between delay and high snr capacity in quantized mimo systems"
    - "gaussian multiple access channels with one bit quantizer at the receiver"
    - "on mimo channel capacity with output quantization constraints"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "at low snr asymmetric quantizers are better"

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)