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: "mimo zero forcing performance evaluation using the holonomic gradient method"
  Abstract: For multiple-input–multiple-output (MIMO) spatial-multiplexing transmission, zero-forcing (ZF) detection is appealing because of its low complexity. Our recent MIMO ZF performance analysis for Rician–Rayleigh fading, which is relevant in heterogeneous networks, has yielded for the ZF outage probability and ergodic capacity infinite-series expressions. Because they arose from expanding the confluent hypergeometric function    $_{1}\!F_{1} (\cdot,\cdot,\sigma) $   around 0, they do not converge numerically at realistically high Rician    $K$  -factor values. Therefore, herein, we seek to take advantage of the fact that    $_{1}\!F_{1} (\cdot,\cdot,\sigma)$   satisfies a differential equation, i.e., it is a   holonomic   function. Holonomic functions can be computed by the   holonomic gradient method   (HGM) , i.e., by numerically solving the satisfied differential equation. Thus, we first reveal that the moment generating function (m.g.f.) and probability density function (p.d.f.) of the ZF signal-to-noise ratio (SNR) are holonomic. Then, from the differential equation for    $_{1}\!F_{1} (\cdot,\cdot,\sigma) $  , we deduce those satisfied by the SNR m.g.f. and p.d.f. and demonstrate that the HGM helps compute the p.d.f. accurately at practically relevant values of    $K$  . Finally, numerical integration of the SNR p.d.f. produced by HGM yields accurate ZF outage probability and ergodic capacity results.

Connected through hyperedge HE-1 (induced by source paper "exact sf zf analysis and computer algebra aided evaluation in rank 1 los rician fading"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "massive mimo ten myths and one critical question"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "schur complement based analysis of mimo zero forcing for rician fading"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "exact mimo zero forcing detection analysis for transmit correlated rician fading"
    - "interactive interference alignment"

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)