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 "sinr statistics of correlated mimo linear receivers"):
  Abstract: Linear receivers offer a low complexity option for multi-antenna communication systems. Therefore, understanding the outage behavior of the corresponding SINR is important in a fading mobile environment. In this paper we introduce a large deviations method, valid nominally for a large number M of antennas, which provides the probability density of the SINR of Gaussian channel MIMO Minimum Mean Square Error (MMSE) and zero-forcing (ZF) receivers, with arbitrary transmission power profiles and in the presence of receiver antenna correlations. This approach extends the Gaussian approximation of the SINR, valid for large M asymptotically close to the center of the distribution, obtaining the non-Gaussian tails of the distribution. Our methodology allows us to calculate the SINR distribution to next-to-leading order (O(1/M)) and showcase the deviations from approximations that have appeared in the literature (e.g. the Gaussian or the generalized Gamma distribution). We also analytically evaluate the outage probability, as well as the uncoded bit-error-rate. We find that our approximation is quite accurate even for the smallest antenna arrays (2x2).

Includes members:
  Member "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "covariance aided csi acquisition with non orthogonal pilots in massive mimo a large system performance analysis")
  Member "on the precoder design of flat fading mimo systems equipped with mmse receivers a large system approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis and location optimization for massive mimo systems with circularly distributed antennas")
  Member "tails of random matrix diagonal elements the case of the wishart inverse":
    also connected through other hyperedges:
      (none)
  Member "a deterministic equivalent for the analysis of correlated mimo multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation")
  Member "on the capacity achieving covariance matrix for frequency selective mimo channels using the asymptotic approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "asymptotic performance of linear receivers in mimo fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comments on physical layer cryptography through massive mimo")
  Member "performance of mmse mimo receivers a large n analysis for correlated channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fifty years of mimo detection the road to large scale mimos")
  Member "ber and outage probability approximations for lmmse detectors on correlated mimo channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "max min sinr in large scale single cell mu mimo asymptotic analysis and low complexity transceivers")

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)