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: "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.

Connected through hyperedge HE-1 (induced by source paper "zero crossing modulation for communication with temporally oversampled 1 bit quantization"):
  with members:
    - "multiple parameter estimation with quantized channel output"

Connected through hyperedge HE-2 (induced by source paper "performance analysis for channel estimation with 1 bit adc and unknown quantization threshold"):
  with members:
    - "performance analysis for time of arrival estimation with oversampled low complexity 1 bit a d conversion"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "performance analysis for pilot based 1 bit channel estimation with unknown quantization threshold"
    - "doa parameter estimation with 1 bit quantization bounds methods and the exponential replacement"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "a general framework for transmission with transceiver distortion and some applications"

Connected through hyperedge HE-3 (induced by source paper "wireless channel estimation with low complexity 1 bit adc and unknown quantization threshold"):
  with members:
    - "performance analysis for time of arrival estimation with oversampled low complexity 1 bit a d conversion"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "performance analysis for pilot based 1 bit channel estimation with unknown quantization threshold"
    - "doa parameter estimation with 1 bit quantization bounds methods and the exponential replacement"
    - "one bit massive mimo channel estimation and high order modulations"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "overdemodulation for high performance receivers with low resolution adc"

Connected through hyperedge HE-4 (induced by source paper "doa parameter estimation with 1 bit quantization bounds methods and the exponential replacement"):
  with members:
    - "a lower bound for the fisher information measure"

Connected through hyperedge HE-5 (induced by source paper "asymptotic performance analysis for 1 bit bayesian smoothing"):
  with members:
    - "multiple parameter estimation with quantized channel output"

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)