human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "millimeter wave mimo channel estimation based on adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beam discovery using linear block codes for millimeter wave communication networks")
  Member "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure communication for spatially sparse millimeter wave massive mimo channels via hybrid precoding")
  Member "beamspace channel estimation for millimeter wave massive mimo systems with lens antenna array":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi cell hybrid millimeter wave systems pilot contamination and interference mitigation")
  Member "a lse and sparse message passing based channel estimation for mmwave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems")
  Member "an overview of signal processing techniques for millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "subspace estimation and decomposition for large millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a hybrid model based and data driven approach to spectrum sharing in mmwave cellular networks")
  Member "hybrid mimo architectures for millimeter wave communications phase shifters or switches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "task based quantization with application to mimo receivers")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")

Question: Please tell me which class the source paper 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)