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 calibration based cascaded channel estimation for reconfigurable intelligent surface assisted multiuser mimo"):
  Abstract: Reconfigurable intelligent surface (RIS) is envisioned to be an essential component of the paradigm for beyond 5G networks as it can potentially provide similar or higher array gains with much lower hardware cost and energy consumption compared with the massive multiple-input multiple-output (MIMO) technology. In this paper, we focus on one of the fundamental challenges, namely the channel acquisition, in an RIS-assisted multiuser MIMO system. The state-of-the-art channel acquisition approach in such a system with fully passive RIS elements estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem in the RIS-assisted multiuser MIMO system as a matrix-calibration based matrix factorization task. By exploiting the information on the slow-varying channel components and the hidden channel sparsity, we propose a novel message-passing based algorithm to factorize the cascaded channels. Furthermore, we present an analytical framework to characterize the theoretical performance bound of the proposed estimator in the large-system limit. Finally, we conduct simulations to verify the high accuracy and efficiency of the proposed algorithm.

Includes members:
  Member "cascaded channel estimation for large intelligent metasurface assisted massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "passive beamforming and information transfer via large intelligent surface":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parallel factor decomposition channel estimation in ris assisted multi user miso communication")
  Member "enabling large intelligent surfaces with compressive sensing and deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intelligent reflecting surface assisted secure wireless communications with multiple transmit and multiple receive antennas")
  Member "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "millimeter wave communications with an intelligent reflector performance optimization and distributional reinforcement learning")
  Member "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "reconfigurable intelligent surfaces for energy efficiency in wireless communication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mimo noma networks relying on reconfigurable intelligent surface a signal cancellation based design")
  Member "large intelligent surface assisted wireless communication exploiting statistical csi":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligent reflecting surface assisted secure wireless communications with multiple transmit and multiple receive antennas")
  Member "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectral method for phase retrieval an expectation propagation perspective")

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)