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 "asymptotic parameter tracking performance with measurement data of 1 bit resolution"):
  Abstract: The problem of signal parameter estimation and tracking with measurement data of low resolution is considered. In comparison to an ideal receiver with infinite receive resolution, the performance loss of a simplistic receiver with 1-bit resolution is investigated. For the case where the measurement data is preprocessed by a symmetric hard-limiting device with 1-bit output, it is well-understood that the performance for low SNR channel parameter estimation degrades moderately by   $2/\pi(-{\hbox{1.96~ dB}})$  . Here, we show that the 1-bit quantization loss can be significantly smaller if information about the temporal evolution of the channel parameters is taken into account in the form of a state-space model. By the analysis of a Bayesian bound for the achievable tracking performance, we attain the result that the quantization loss in dB is in general smaller by a factor of two if the channel evolution is slow. For the low SNR regime, this is equivalent to a reduced loss of   $\sqrt{2/\pi}(-{\hbox{0.98 dB}})$  . By simulating non-linear filtering algorithms for a satellite-based ranging application and a UWB channel estimation problem, both with low-complexity 1-bit analog-to-digital converter (ADC) at the receiver, we verify that the analytical characterization of the tracking error is accurate. This shows that the performance loss due to observations with low amplitude resolution can, in practice, be much less pronounced than indicated by classical results. Finally, we discuss the implication of the result for medium SNR applications like channel estimation in the context of mobile wireless communications.

Includes members:
  Member "overdemodulation for high performance receivers with low resolution adc":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance analysis of mixed adc massive mimo systems over rician fading channels")
  Member "a general framework for transmission with transceiver distortion and some applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sensitivity analysis for binary sampling systems via quantitative fisher information lower bounds")
  Member "at low snr asymmetric quantizers are better":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "multiple parameter estimation with quantized channel output":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on throughput of millimeter wave mimo systems with low resolution adcs")
  Member "increased capacity per unit cost by oversampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bandlimited communication with one bit quantization and oversampling transceiver design and performance evaluation")

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)