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: "tails of random matrix diagonal elements the case of the wishart inverse"
  Abstract: We analytically compute the large-deviation probability of a diagonal matrix element of two cases of random matrices, namely $\beta=[\vec H^\dagger\vec H]^{-1}_{11}$ and $\gamma=[\vec I_N+\rho\vec H^\dagger\vec H]^{-1}_{11}$, where $\vec H$ is a $M\times N$ complex Gaussian matrix with independent entries and $M\geq N$. These diagonal entries are related to the "signal to interference and noise ratio" (SINR) in multi-antenna communications. They depend not only on the eigenvalues but also on the corresponding eigenfunction weights, which we are able to evaluate on average constrained on the value of the SINR. We also show that beyond a lower and upper critical value of $\beta$, $\gamma$, the maximum and minimum eigenvalues, respectively, detach from the bulk. Responsible for this detachment is the fact that the corresponding eigenvalue weight becomes macroscopic (i.e. O(1)), and hence exerts a strong repulsion to the eigenvalue.

Connected through hyperedge HE-1 (induced by source paper "sinr statistics of correlated mimo linear receivers"):
  with members:
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "on the precoder design of flat fading mimo systems equipped with mmse receivers a large system approach"
    - "a deterministic equivalent for the analysis of correlated mimo multiple access channels"
    - "on the capacity achieving covariance matrix for frequency selective mimo channels using the asymptotic approach"
    - "asymptotic performance of linear receivers in mimo fading channels"
    - "performance of mmse mimo receivers a large n analysis for correlated channels"
    - "ber and outage probability approximations for lmmse detectors on correlated mimo channels"
    - "a central limit theorem for the sinr at the lmmse estimator output for large dimensional signals"

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)