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: "a pessimistic approximation for the fisher information measure"
  Abstract: The problem of determining the intrinsic quality of a signal processing system with respect to the inference of an unknown deterministic parameter   $\theta$   is considered. While the Fisher information measure   $F(\theta)$    forms a classical tool for such a problem, direct computation of the information measure can become difficult in various situations. For the estimation theoretic performance analysis of nonlinear measurement systems, the form of the likelihood function can make the calculation of the information measure    $F(\theta)$   challenging. In situations where no closed-form expression of the statistical system model is available, the analytical derivation of    $F(\theta)$   is not possible at all. Based on the Cauchy–Schwarz inequality, we derive an alternative information measure    $S(\theta)$  . It provides a lower bound on the Fisher information   $F(\theta)$   and has the property of being evaluated with the mean, the variance, the skewness, and the kurtosis of the system model at hand. These entities usually exhibit good mathematical tractability or can be determined at low-complexity by real-world measurements in a calibrated setup. With various examples, we show that   $S(\theta)$    provides a good conservative approximation for   $F(\theta)$    and outline different estimation theoretic problems where the presented information bound turns out to be useful.

Connected through hyperedge HE-1 (induced by source paper "sensitivity analysis for binary sampling systems via quantitative fisher information lower bounds"):
  with members:
    - "measurement driven quality assessment of nonlinear systems by exponential replacement"
    - "doa parameter estimation with 1 bit quantization bounds methods and the exponential replacement"
    - "a lower bound for the fisher information measure"
    - "gaussian assumption the least favorable but the most useful lecture notes"
    - "a general framework for transmission with transceiver distortion and some applications"
    - "information theoretic proofs of entropy power inequalities"
    - "multiple parameter estimation with quantized channel output"

Connected through hyperedge HE-2 (induced by source paper "optimization of massive full dimensional mimo for positioning and communication"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "sectoring in multi cell massive mimo systems"
    - "layered downlink precoding for c ran systems with full dimensional mimo"
    - "positioning for the internet of things a 3gpp perspective"
    - "downlink achievable rate analysis in massive mimo systems with one bit dacs"
    - "optimal fronthaul quantization for cloud radio positioning"
    - "parallel and distributed methods for nonconvex optimization part ii applications"
    - "quantized precoding for massive mu mimo"

Connected through hyperedge HE-3 (induced by source paper "fisher information bounds with applications in nonlinear learning compression and inference"):
  with members:
    - "measurement driven quality assessment of nonlinear systems by exponential replacement"
    - "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"
    - "a lower bound for the fisher information measure"
    - "overdemodulation for high performance receivers with low resolution adc"
    - "gaussian assumption the least favorable but the most useful lecture notes"

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)