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: "convergence and fluctuations of regularized tyler estimators"
  Abstract: This paper studies the behavior of regularized Tyler estimators (RTEs) of scatter matrices. The advantages of these estimators are twofold. First, they guarantee by construction a good conditioning of the estimate and second, being derivatives of robust Tyler estimators, they inherit their robustness properties, notably their resilience to outliers. Nevertheless, one major problem that poses the use of RTEs is represented by the question of setting the regularization parameter ρ. While a high value of ρ is likely to push all the eigenvalues away from zero, it comes at the cost of a larger bias with respect to the population covariance matrix. A deep understanding of the statistics of RTEs is essential to come up with appropriate choices for the regularization parameter. This is not an easy task and requires working under asymptotic regimes wherein the number of observations n and/or their size N increase together. First asymptotic results have recently been obtained when N and n are large and commensurable. Interestingly, no results concerning the regime of n going to infinity with N fixed exist. This motivates our work. In particular, we prove in this paper that the RTEs converge to a deterministic matrix when n→ ∞ with N fixed, which is expressed as a function of the theoretical covariance matrix. We also derive the fluctuations of the RTEs around this limit and establish that these fluctuations converge in distribution to a multivariate Gaussian distribution with parameters depending on the population covariance and the regularization coefficient.

Connected through hyperedge HE-1 (induced by source paper "fluctuations of the snr at the output of the mvdr with regularized tyler estimators"):
  with members:
    - "a clt on the snr of diagonally loaded mvdr filters"

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)