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 "fisher information bounds with applications in nonlinear learning compression and inference"):
  Abstract: The problem how to derive a generic lower bound for the Fisher information measure is considered. We review a recent approach by two examples and identify a connection between the construction of strong Fisher information bounds and the sufficient statistics of the underlying system model. In order to present the problem of such information bounds within a broad scope, we discuss the properties of the Fisher information measure for distributions belonging to the exponential family. Under this restriction, we establish an identity connecting Fisher information, the natural parameters and the sufficient statistics of the system model. Replacing an arbitrary system model by an equivalent distribution within the exponential family, we then derive a general lower bound for the Fisher information measure. With the optimum estimation theoretic model matching rule we show how to obtain a strong version of the information bound. We then demonstrate different applications of the proposed conservative likelihood framework and the derived Fisher information bound. In particular, we discuss how to determine the minimum guaranteed inference capability of a memoryless system with unknown statistical output model and show how to achieve this pessimistic performance assessment with a root-n consistent estimator operating on a nonlinear compressed version of the observed data. Finally, we identify that the derived conservative maximum-likelihood algorithm can be formulated as a special version of Hansen’s generalized method of moments.

Includes members:
  Member "a pessimistic approximation for the fisher information measure":
    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 "measurement driven quality assessment of nonlinear systems by exponential replacement":
    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 "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 "sensitivity analysis for binary sampling systems via quantitative fisher information lower bounds")
  Member "capacity analysis of one bit quantized mimo systems with transmitter channel state information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "a lower bound for the fisher information measure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sensitivity analysis for binary sampling systems via quantitative fisher information lower bounds")
  Member "overdemodulation for high performance receivers with low resolution adc":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "performance analysis of mixed adc massive mimo systems over rician fading channels")
  Member "gaussian assumption the least favorable but the most useful lecture notes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sensitivity analysis for binary sampling systems via quantitative fisher information lower bounds")

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)