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 "matrix completion based channel estimation for mmwave communication systems with array inherent impairments"):
  Abstract: Hybrid massive MIMO structures with reduced hardware complexity and power consumption have been widely studied as a potential candidate for millimeter wave communications. Channel estimators that require knowledge of the array response, such as those using compressive sensing (CS) methods, may suffer from performance degradation when array-inherent impairments bring unknown phase errors and gain errors to the antenna elements. In this paper, we design matrix completion (MC)-based channel estimation schemes which are robust against the array-inherent impairments. We first design an open-loop training scheme that can sample entries from the effective channel matrix randomly and is compatible with the phase shifter-based hybrid system. Leveraging the low-rank property of the effective channel matrix, we then design a channel estimator based on the generalized conditional gradient framework and the alternating minimization approach. The resulting estimator is immune to array-inherent impairments and can be implemented to systems with any array shapes for its independence of the array response. In addition, we extend our design to sample a transformed channel matrix following the concept of inductive matrix completion (IMC), which can be solved efficiently using our proposed channel estimator and can achieve similar performance with a lower requirement of the dynamic range of the transmission power per antenna. Numerical results demonstrate the advantages of our proposed MC-based channel estimator in terms of estimation performance, computational complexity, and robustness against array-inherent impairments over the orthogonal matching pursuit-based CS channel estimator.

Includes members:
  Member "millimeter wave channel estimation via exploiting joint sparse and low rank structures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beamforming design for large scale antenna arrays using deep learning")
  Member "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beam squint and channel estimation for wideband mmwave massive mimo ofdm systems")
  Member "channel estimation for hybrid architecture based wideband millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "subspace estimation and decomposition for large millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hybrid model based and data driven approach to spectrum sharing in mmwave cellular networks")
  Member "codebook design for millimeter wave channel estimation with hybrid precoding structure":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "channel estimation and hybrid precoding for distributed phased arrays based mimo wireless communications")
  Member "channel estimation for mmwave massive mimo based access and backhaul in ultra dense network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully partially connected hybrid beamforming architectures for mmwave mu mimo")
  Member "joint csit acquisition based on low rank matrix completion for fdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "low rank matrix completion a contemporary survey")

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)