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: "cfo estimation for massive mu mimo systems"
  Abstract: Low-complexity carrier frequency offset (CFO) estimation/compensation in massive multi-user (MU) multiple-input multiple-output (MIMO) systems is a challenging problem. The existing CFO estimation/compensation strategies for conventional small MIMO systems experience tremendous increase in complexity with increasing number of the user terminals (UTs), $K$ and the number of base station (BS) antennas, $M$ (i.e. in massive MIMO regime). In this paper, we devise a \textit{low-complexity} algorithm for CFO estimation using the pilots received at the BS during a special uplink slot. The total per-channel use complexity of the proposed algorithm increases only linearly with increasing $M$ and is independent of $K$. Analytical expression is derived for the mean square error (MSE) of the proposed CFO estimator. Further analysis reveals that the MSE of the proposed estimator decreases with increasing $M$ (fixed $K$) and increasing $K$ (fixed $M$ and CFO pilot sequence length $\gg K$). We also derive an achievable information sum-rate expression of the time-reversal maximal-ratio-combining (TR-MRC) detector in the presence of residual CFO errors, resulting from the proposed CFO estimation/compensation algorithm. Analysis of this information rate expression reveals that an $\mathcal{O}(\sqrt{M})$ array gain is achievable. This new result is interesting since the best possible array gain under an ideal no CFO condition is also known to be $\mathcal{O}(\sqrt{M})$.

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)