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: "2d unitary esprit based super resolution channel estimation for millimeter wave massive mimo with hybrid precoding"
  Abstract: Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with hybrid precoding is a promising technique for the future 5G wireless communications. Due to a large number of antennas but a much smaller number of radio frequency (RF) chains, estimating the high-dimensional mmWave massive MIMO channel will bring the large pilot overhead. To overcome this challenge, this paper proposes a super-resolution channel estimation scheme based on two-dimensional (2D) u- nitary ESPRIT algorithm. By exploiting the angular sparsity of mmWave channels, the continuously distributed angle of arrivals/departures (AoAs/AoDs) can be jointly estimated with high accuracy. Specifically, by designing the uplink training signals at both base station (BS) and mobile station (MS), we first use low pilot overhead to estimate a low-dimensional effective channel, which has the same shift-invariance of array response as the high-dimensional mmWave MIMO channel to be estimated. From the low-dimensional effective channel, the super- resolution estimates of AoAs and AoDs can be jointly obtained by exploiting the 2D unitary ESPRIT channel estimation algorithm. Furthermore, the associated path gains can be acquired based on the least squares (LS) criterion. Finally, we can reconstruct the high-dimensional mmWave MIMO channel according to the obtained AoAs, AoDs, and path gains. Simulation results have confirmed that the proposed scheme is superior to conventional schemes with a much lower pilot overhead.

Connected through hyperedge HE-1 (induced by source paper "machine learning based hybrid precoding for mmwave mimo ofdm with dynamic subarray"):
  with members:
    - "energy efficient transceiver design for hybrid sub array architecture mimo systems"
    - "compressive sensing techniques for next generation wireless communications"
    - "codebook based hybrid precoding for millimeter wave multiuser systems"
    - "codebook design for millimeter wave channel estimation with hybrid precoding structure"
    - "dynamic subarrays for hybrid precoding in wideband mmwave mimo systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "mmwave massive mimo based wireless backhaul for the 5g ultra dense network"

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)