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: "low complexity hybrid precoding and channel estimation based on hierarchical multi beam search for millimeter wave mimo systems"
  Abstract: In millimeter-wave (mmWave) MIMO systems, while a hybrid digital/analog precoding structure offers the potential to increase the achievable rate, it also faces the challenge of the need of a low-complexity design. In specific, the hybrid precoding may require matrix operations with a scale of antenna size, which is generally large in mmWave communication. Moreover, the channel estimation is also rather time consuming due to the large number of antennas at both Tx/Rx sides. In this paper, a low-complexity hybrid precoding and channel estimation approach is proposed. In the channel estimation phase, a hierarchical multi-beam search scheme is proposed to fast acquire $N_{\rm{S}}$ (the number of streams) multipath components (MPCs)/clusters with the highest powers. In the hybrid precoding phase, the analog and digital precodings are decoupled. The analog precoding is designed to steer along the $N_{\rm{S}}$ acquired MPCs/clusters at both Tx/Rx sides, shaping an equivalent $N_{\rm{S}}\times N_{\rm{S}}$ baseband channel, while the digital precoding performs operations in the baseband with the reduced-scale channel. Performance evaluations show that, compared with a state-of-the-art scheme, while achieving a close or even better performance when the number of radio-frequency (RF) chains or streams is small, both the computational complexity of the hybrid precoding and the time complexity of the channel estimation are greatly reduced.

Connected through hyperedge HE-1 (induced by source paper "codebook design for millimeter wave channel estimation with hybrid precoding structure"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "iterative eigenvalue decomposition and multipath grouping tx rx joint beamformings for millimeter wave communications"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "massive mimo for next generation wireless systems"

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)