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: "ber and outage probability approximations for lmmse detectors on correlated mimo channels"
  Abstract: This paper is devoted to the study of the performance of the Linear Minimum Mean-Square Error receiver for (receive) correlated Multiple-Input Multiple-Output systems. By the random matrix theory, it is well-known that the Signal-to-Noise Ratio (SNR) at the output of this receiver behaves asymptotically like a Gaussian random variable as the number of receive and transmit antennas converge to +$\infty$ at the same rate. However, this approximation being inaccurate for the estimation of some performance metrics such as the Bit Error Rate and the outage probability, especially for small system dimensions, Li et al. proposed convincingly to assume that the SNR follows a generalized Gamma distribution which parameters are tuned by computing the first three asymptotic moments of the SNR. In this article, this technique is generalized to (receive) correlated channels, and closed-form expressions for the first three asymptotic moments of the SNR are provided. To obtain these results, a random matrix theory technique adapted to matrices with Gaussian elements is used. This technique is believed to be simple, efficient, and of broad interest in wireless communications. Simulations are provided, and show that the proposed technique yields in general a good accuracy, even for small system dimensions.

Connected through hyperedge HE-1 (induced by source paper "max min sinr in large scale single cell mu mimo asymptotic analysis and low complexity transceivers"):
  with members:
    - "large system analysis of the energy consumption distribution in multi user mimo systems with mobility"
    - "large system analysis of base station cooperation for power minimization"
    - "optimal multiuser transmit beamforming a difficult problem with a simple solution structure"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "massive mimo for next generation wireless systems"
    - "joint beamforming and power control in coordinated multicell max min duality effective network and large system transition"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-2 (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"
    - "tails of random matrix diagonal elements the case of the wishart inverse"
    - "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"
    - "a central limit theorem for the sinr at the lmmse estimator output for large dimensional signals"

Connected through hyperedge HE-3 (induced by source paper "a clt on the snr of diagonally loaded mvdr filters"):
  with members:
    - "a central limit theorem for the sinr at the lmmse estimator output for large dimensional signals"
    - "a new approach for capacity analysis of large dimensional multi antenna channels"

Connected through hyperedge HE-4 (induced by source paper "on the precoder design of flat fading mimo systems equipped with mmse receivers a large system approach"):
  with members:
    - "asymptotic performance of linear receivers in mimo fading channels"
    - "performance of mmse mimo receivers a large n analysis for correlated channels"
    - "a new approach for capacity analysis of large dimensional multi antenna channels"
    - "on the outage capacity of correlated multiple path mimo channels"

Connected through hyperedge HE-5 (induced by source paper "tails of random matrix diagonal elements the case of the wishart inverse"):
  with members:
    - "living at the edge a large deviations approach to the outage mimo capacity"
    - "asymptotic performance of linear receivers in mimo fading channels"
    - "a central limit theorem for the sinr at the lmmse estimator output for large dimensional signals"
    - "a new approach for capacity analysis of large dimensional multi antenna channels"

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)