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 "performance analysis of target parameters estimation using multiple widely separated antenna arrays"):
  Abstract: Target parameter estimation performance is investigated for a radar employing a set of widely separated transmitting and receiving antenna arrays. Cases with multiple extended targets are considered under two signal model assumptions: stochastic and deterministic. The general expressions for the corresponding Cramer-Rao lower bound (CRLB) and the asymptotic properties of the maximum-likelihood (ML) estimator are derived for a radar with Mt arrays of Lt transmitting elements and Mr arrays of Lr receiving elements for both types of signal models. It is shown that for an infinitely large product MtMr, and a finite Lr, the ML estimator is consistent and efficient under the stochastic model, while the deterministic model requires MtMr to be finite and Lr to be infinitely large in order to guarantee consistency and efficiency. Monte Carlo simulations further investigate the estimation performance of the proposed radar configuration in practical scenarios with finite MtMr and Lr, and a fixed total number of available receiving antenna elements, MrLr. The numerical results demonstrate that grouping receiving elements into properly sized arrays reduces the mean

Includes members:
  Member "optimal joint target detection and parameter estimation by mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recursive distributed detection for composite hypothesis testing nonlinear observation models in additive gaussian noise")
  Member "target localization accuracy gain in mimo radar based systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "suboptimum low complexity joint multi target detection and localization for noncoherent mimo radar with widely separated antennas")
  Member "mimo radar using compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging")

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)