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: "backing off from infinity performance bounds via concentration of spectral measure for random mimo channels"
  Abstract: The performance analysis of random vector channels, particularly multiple-input-multiple-output (MIMO) channels, has largely been established in the asymptotic regime of large channel dimensions, due to the analytical intractability of characterizing the exact distribution of the objective performance metrics. This paper exposes a new non-asymptotic framework that allows the characterization of many canonical MIMO system performance metrics to within a narrow interval under moderate-to-large channel dimensionality, provided that these metrics can be expressed as a separable function of the singular values of the matrix. The effectiveness of our framework is illustrated through two canonical examples. Specifically, we characterize the mutual information and power offset of random MIMO channels, as well as the minimum mean squared estimation error of MIMO channel inputs from the channel outputs. Our results lead to simple, informative, and reasonably accurate control of various performance metrics in the finite-dimensional regime, as corroborated by the numerical simulations. Our analysis framework is established via the concentration of spectral measure phenomenon for random matrices uncovered by Guionnet and Zeitouni, which arises in a variety of random matrix ensembles irrespective of the precise distributions of the matrix entries.

Connected through hyperedge HE-1 (induced by source paper "minimax capacity loss under sub nyquist universal sampling"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "living at the edge a large deviations approach to the outage mimo capacity"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "on the minimax capacity loss under sub nyquist universal sampling"):
  with members:
    - "sampling rate distortion"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "living at the edge a large deviations approach to the outage mimo capacity"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "increased capacity per unit cost by oversampling"
    - "condition numbers of gaussian random matrices"

Connected through hyperedge HE-3 (induced by source paper "capacity scaling in mimo systems with general unitarily invariant random matrices"):
  with members:
    - "applications of large random matrices in communications engineering"
    - "outage capacity for the optical mimo channel"
    - "the jacobi mimo channel"

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)