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: "on regret of parametric mismatch in minimum mean square error estimation"
  Abstract: This paper studies the effect of parametric mismatch in minimum mean square error (MMSE) estimation. In particular, we consider the problem of estimating the input signal from the output of an additive white Gaussian channel whose gain is fixed, but unknown. The input distribution is known, and the estimation process consists of two algorithms. First, a channel estimator blindly estimates the channel gain using past observations. Second, a mismatched MMSE estimator, optimized for the estimated channel gain, estimates the input signal. We analyze the regret, i.e., the additional mean square error, that is raised in this process. We derive upper-bounds on both absolute and relative regrets. Bounds are expressed in terms of the Fisher information. We also study regret for unbiased, efficient channel estimators, and derive a simple trade-off between Fisher information and relative regret. This trade-off shows that the product of a certain function of relative regret and Fisher information equals the signal-to-noise ratio, independent of the input distribution. The trade-off relation implies that higher Fisher information results to smaller expected relative regret.

Connected through hyperedge HE-1 (induced by source paper "estimation in poisson noise properties of the conditional mean estimator"):
  with members:
    - "mutual information relative entropy and estimation error in semi martingale channels"
    - "a de bruijn identity for discrete random variables"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "bounds on the lambert function and their application to the outage analysis of user cooperation"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "compressed sensing performance bounds under poisson noise"
    - "mutual information relative entropy and estimation in the poisson channel"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "analysis of mismatched estimation errors using gradients of partition functions"):
  with members:
    - "optimum estimation via gradients of partition functions and information measures a statistical mechanical perspective"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "mmse of bad codes"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "mutual information relative entropy and estimation in the poisson channel"
    - "message passing algorithms for compressed sensing"
    - "universal minimax discrete denoising under channel uncertainty"
    - "mutual information and minimum mean square error in gaussian channels"

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)