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 "phase quantized block noncoherent communication"):
  Abstract: Analog-to-digital conversion (ADC) is a key bottleneck in scaling DSP-centric receiver architectures to multiGigabit/s speeds. Recent information-theoretic results, obtained under ideal channel conditions (perfect synchronization, no dispersion), indicate that low-precision ADC (1-4 bits) could be a suitable choice for designing such high speed systems. In this work, we study the impact of employing low-precision ADC in a {\it carrier asynchronous} system. Specifically, we consider transmission over the block noncoherent Additive White Gaussian Noise (AWGN) channel, and investigate the achievable performance under low-precision output quantization. We focus attention on an architecture in which the receiver quantizes {\it only the phase} of the received signal: this has the advantage of being implementable without automatic gain control, using multiple 1-bit ADCs preceded by analog multipliers. For standard uniform Phase Shift Keying (PSK) modulation, we study the structure of the transition density of the resulting phase-quantized block noncoherent channel. Several results, based on the symmetry inherent in the channel model, are provided to characterize this transition density. Low-complexity procedures for computing the channel capacity, and for block demodulation, are obtained using these results. Numerical computations are performed to compare the performance of quantized and unquantized systems, for different quantization precisions, and different block lengths. It is observed, for example, that with QPSK modulation, 8-bin phase quantization of the received signal recovers about 80-85% of the capacity attained with unquantized observations, while 12-bin phase quantization recovers more than 90% of the unquantized capacity. Dithering the constellation is shown to improve the performance in the face of drastic quantization.

Includes members:
  Member "a general framework for transmission with transceiver distortion and some applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sensitivity analysis for binary sampling systems via quantitative fisher information lower bounds")
  Member "asymmetric quantizers are better at low snr":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a general framework for transmission with transceiver distortion and some applications")
  Member "towards a communication theoretic understanding of system level power consumption":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient beam coordination strategies with rate dependent processing power")
  Member "on block noncoherent communication with low precision phase quantization at the receiver":
    also connected through other hyperedges:
      (none)
  Member "capacity of the discrete time awgn channel under output quantization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new equivalent model of quantizer with noisy input and its application for adc resolution determination in an uplink mimo receiver")

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)