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 "on the equivalence of maximum snr and mmse estimation applications to additive non gaussian channels and quantized observations"):
  Abstract: The minimum mean-squared error (MMSE) is one of the most popular criteria for Bayesian estimation. Conversely, the signal-to-noise ratio (SNR) is a typical performance criterion in communications, radar, and generally detection theory. In this paper, we first formalize an SNR criterion to design an estimator, and then we prove that there exists an equivalence between MMSE and maximum-SNR estimators, for any statistics. We also extend this equivalence to specific classes of suboptimal estimators, which are expressed by a basis expansion model (BEM). Then, by exploiting an orthogonal BEM for the estimator, we derive the MMSE estimator constrained to a given quantization resolution of the noisy observations, and we prove that this suboptimal MMSE estimator tends to the optimal MMSE estimator that uses an infinite resolution of the observation. Besides, we derive closed-form expressions for the mean-squared error (MSE) and for the SNR of the proposed suboptimal estimators, and we show that these expressions constitute tight, asymptotically exact, bounds for the optimal MMSE, and maximum SNR.

Includes members:
  Member "bayesian estimation of a gaussian source in middleton s class a impulsive noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non linear transformations of gaussians and gaussian mixtures with implications on estimation and information theory")
  Member "on conditions for linearity of optimal estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the minimum mean p th error in gaussian noise channels and its applications")
  Member "pointwise relations between information and estimation in gaussian noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a generalized expression for the gradient of mutual information with the application in multiple access channels")
  Member "estimation in gaussian noise properties of the minimum mean square error":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "mutual information and minimum mean square error in gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")

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)