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 "sensitivity analysis for binary sampling systems via quantitative fisher information lower bounds"):
  Abstract: This article addresses the sensitivity of sensor systems with minimal signal digitization complexity regarding the estimation of analog model parameters. Digital measurements are exclusively available in a hard-limited form, and the parameters of the analog received signals shall be inferred through efficient algorithms. As a benchmark, the achievable estimation accuracy is to be assessed based on theoretical error bounds. To this end, characterization of the parametric likelihood is required, which forms a challenge for multivariate binary distributions. In this context, we analyze the Fisher information matrix of the exponential family and derive a conservative approximation for arbitrary models. The conservative information matrix rests on a surrogate exponential family, defined by two equivalences to the real data-generating system. This probabilistic notion enables designing estimators that consistently achieve the sensitivity level defined by the inverse of the conservative information matrix without characterizing the distributions involved. For parameter estimation with multivariate binary samples, using an equivalent quadratic exponential distribution tames the computational complexity of the conservative information matrix such that a quantitative assessment of the achievable error level becomes tractable. We exploit this for the performance analysis concerning signal parameter estimation with an array of low-complexity binary sensors by examining the achievable sensitivity in comparison to an ideal system featuring receivers supporting data acquisition with infinite amplitude resolution. Additionally, we demonstrate data-driven sensitivity analysis through the presented framework by learning the guaranteed achievable performance when processing sensor data obtained with recursive binary sampling schemes as implemented in $\Sigma\Delta$-modulating analog-to-digital converters.

Includes members:
  Member "a pessimistic approximation for the fisher information measure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimization of massive full dimensional mimo for positioning and communication")
  Member "measurement driven quality assessment of nonlinear systems by exponential replacement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fisher information bounds with applications in nonlinear learning compression and inference")
  Member "doa parameter estimation with 1 bit quantization bounds methods and the exponential replacement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance analysis for channel estimation with 1 bit adc and unknown quantization threshold")
  Member "a lower bound for the fisher information measure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "investigation of channel estimation techniques with 1 bit quantization and oversampling for multiple antenna systems")
  Member "gaussian assumption the least favorable but the most useful lecture notes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "decorrelation of neutral vector variables theory and applications")
  Member "a general framework for transmission with transceiver distortion and some applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a regression approach to certain information transmission problems")
  Member "information theoretic proofs of entropy power inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vector gaussian successive refinement with degraded side information")
  Member "multiple parameter estimation with quantized channel output":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on throughput of millimeter wave mimo systems with low resolution adcs")

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)