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 "reliable ofdm transmission with ultra low resolution adc"):
  Abstract: The use of low-resolution analog-to-digital converters (ADCs) can significantly reduce power consumption and hardware cost. However, severe nonlinear distortion due to low-resolution ADCs makes achieving reliable data transmission challenging. Particularly, for orthogonal frequency division multiplexing (OFDM) transmission, the orthogonality among subcarriers is destroyed, which invalidates the conventional linear receivers that highly relies on this orthogonality. In this study, we develop an efficient system architecture for OFDM transmission with ultra-low resolution ADC (e.g., 1-2 bits). A novel channel estimator is proposed to estimate the desired channel parameters without their priori distributions. In particular, we integrate the linear minimum mean squared error channel estimator into the generalized Turbo (GTurbo) framework and derive its corresponding extrinsic information to guarantee the convergence of the GTurbo-based algorithm. We also propose feasible schemes for automatic gain control, noise power estimation, and synchronization. Furthermore, we construct a proof-of-concept prototyping system and conduct over-the-air (OTA) experiments to examine the feasibility/reliability of the entire system. To the best of our knowledge, this is the first work that focused on system design and implementation of communications with low-resolution ADCs worldwide. Numerical simulation and OTA experiment results demonstrate that the proposed system supports reliable OFDM data transmission with ultra-low resolution ADCs.

Includes members:
  Member "joint channel estimation decoding with frequency selective channels and few bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "soft output detection methods for sparse millimeter wave mimo systems with low precision adcs")
  Member "low complexity multiuser qam detection for uplink 1 bit massive mimo":
    also connected through other hyperedges:
      (none)
  Member "a general framework for low resolution receivers for mimo channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "uplink multiuser massive mimo systems with low resolution adcs a coding theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "one bit sphere decoding for uplink massive mimo systems with one bit adcs")
  Member "bayesian optimal data detector for mmwave ofdm system with low resolution adc":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time varying downlink channel tracking for quantized massive mimo networks")
  Member "generalized expectation consistent signal recovery for nonlinear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "denoising based turbo compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random access for massive machine type communications")
  Member "channel estimation and data equalization in frequency selective mimo systems with one bit quantization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bayesian optimal data detector for mmwave ofdm system with low resolution adc")

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)