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: "exact sf zf analysis and computer algebra aided evaluation in rank 1 los rician fading"
  Abstract: We study zero-forcing ( ZF ) detection for multiple input/multiple output (MIMO) spatial multiplexing under transmit-correlated Rician fading for an    $ {N_{\text {R}}}\times {N_{\text {T}}}$    channel matrix with rank-1 line-of-sight component. By using matrix transformations and multivariate statistics, our exact analysis yields the signal-to-noise ratio moment generating function (M.G.F.) as an infinite series of gamma distribution M.G.F.’s and analogous series for  ZF  performance measures, e.g., outage probability and ergodic capacity. However, their numerical convergence is inherently problematic with increasing Rician    $ K $   -factor,    $ {N_{\text {R}}}$   , and    $ {N_{\text {T}}}$   . We circumvent this limitation as follows. First, we derive differential equations satisfied by the performance measures with a novel automated approach employing a computer-algebra tool that implements Grobner basis computation and creative telescoping. These differential equations are then solved with the holonomic gradient method (HGM) from initial conditions computed with the infinite series. We demonstrate that HGM yields more reliable performance evaluation than by infinite series alone and more expeditious than by simulation, for realistic values of    $K$   , and even for    $ {N_{\text {R}}}$    and    $ {N_{\text {T}}}$    relevant to large MIMO systems. We envision extending the proposed approaches for exact analysis and reliable evaluation to more general Rician fading and other transceiver methods.

Connected through hyperedge HE-1 (induced by source paper "distributed spatial multiplexing systems with hardware impairments and imperfect channel estimation under rank 1 rician fading channels"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "achievable rate of rician large scale mimo channels with transceiver hardware impairments"
    - "eigenvalue dynamics of a central wishart matrix with application to mimo systems"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "directional cell discovery in millimeter wave cellular networks"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "on the mimo capacity with residual transceiver hardware impairments"

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)