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 "estimation in poisson noise properties of the conditional mean estimator"):
  Abstract: This paper considers estimation of a random variable in Poisson noise with signal scaling coefficient and dark current as explicit parameters of the noise model. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the scaling coefficient, the dark current parameter, the distribution of the input random variable and channel realizations. #R##N#With respect to the scaling coefficient and the dark current several identities in terms of derivatives are established. For example, it is shown that the derivative of the conditional mean estimator with respect to the dark current parameter is proportional to the conditional variance. Moreover, a version of score function is proposed and a Tweedie-like formula for the conditional expectation is recovered. #R##N#With respect to the distribution several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in the mean squared error, then the input distribution is approximately gamma in the Levy distance.

Includes members:
  Member "mutual information relative entropy and estimation error in semi martingale channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a class of lower bounds for bayesian risk with a bregman loss")
  Member "a de bruijn identity for discrete random variables":
    also connected through other hyperedges:
      (none)
  Member "strong data processing inequalities for input constrained additive noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on data processing and majorization inequalities for f divergences with applications")
  Member "bounds on the lambert function and their application to the outage analysis of user cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments")
  Member "on regret of parametric mismatch in minimum mean square error estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analysis of mismatched estimation errors using gradients of partition functions")
  Member "estimation in gaussian noise properties of the minimum mean square error":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "compressed sensing performance bounds under poisson noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse non negative recovery from biased subgaussian measurements using nnls")
  Member "mutual information relative entropy and estimation in the poisson channel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a class of lower bounds for bayesian risk with a bregman loss")

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)