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: "information measures inequalities and performance bounds for parameter estimation in impulsive noise environments"
  Abstract: Recent studies found that many channels are affected by additive noise that is impulsive in nature and is best explained by heavy-tailed symmetric alpha-stable distributions. Dealing with impulsive noise environments comes with an added complexity with respect to the standard Gaussian environment: the alpha-stable probability density functions have an infinite second moment and the "nice" Hilbert space structure of the space of random variables having a finite second moment is lost along with its tools and methodologies. This is indeed the case in estimation theory where classical tools to quantify performance of an estimator are tightly related to the assumption of finite variance variables. In alpha-stable environments, expressions such as the mean square error and the Cramer-Rao bound are hence problematic. In this work, we tackle the parameter estimation problem in impulsive noise environments and develop novel tools that are tailored to the alpha-stable and heavy-tailed noise environments, tools that coincide with the standard ones adopted in the Gaussian setup, namely a generalized "power" measure and a generalized Fisher information. We generalize known information inequalities commonly used in the Gaussian context: the de Bruijn's identity, the data processing inequality, the Fisher information inequality, the isoperimetric inequality for entropies and the Cramer-Rao bound. Additionally, we derive upper bounds on the differential entropy of independent sums having a stable component. Finally, the new "power" measure is used to shed some light on the additive alpha-stable noise channel capacity in a setup that generalizes the linear average power constrained AWGN channel. Our theoretical findings are paralleled with numerical evaluations of various quantities and bounds using developed {\em Matlab} packages.

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)