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 "unified overview of matrix monotonic optimization for mimo transceivers"):
  Abstract: Matrix-monotonic optimization exploits the monotonic nature of positive semi-definite matrices to derive optimal diagonalizable structures for the matrix variables of matrix-variate optimization problems. Based on the optimal structures derived, the associated optimization problems can be substantially simplified and underlying physical insights can also be revealed. In this paper, a comprehensive overview of the applications of matrix-monotonic optimization to multiple-input multiple-output (MIMO) transceiver design is provided under various power constraints, and matrix-monotonic optimization is investigated for various types of channel state information (CSI) condition. Specifically, three cases are investigated: 1)~both the transmitter and receiver have imperfect CSI; 2)~ perfect CSI is available at the receiver but the transmitter has no CSI; 3)~perfect CSI is available at the receiver but the channel estimation error at the transmitter is norm-bounded. In all three cases, the matrix-monotonic optimization framework can be used for deriving the optimal structures of the optimal matrix variables. Furthermore, based on the proposed framework, three specific applications are given under three types of power constraints. The first is transceiver optimization for the multi-user MIMO uplink, the second is signal compression in distributed sensor networks, and the third is robust transceiver optimization of multi-hop amplify-and-forward cooperative networks.

Includes members:
  Member "a framework for joint design of pilot sequence and linear precoder":
    also connected through other hyperedges:
      (none)
  Member "on weighted mse model for mimo transceiver optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid transceiver optimization for multi hop communications")
  Member "a framework for transceiver designs for multi hop communications with covariance shaping constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hybrid transceiver optimization for multi hop communications")
  Member "fifty years of mimo detection the road to large scale mimos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "model driven deep learning for joint mimo channel estimation and signal detection")
  Member "convex separable problems with linear constraints in signal processing and communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new viewpoint and algorithms for water filling solutions in wireless communications")
  Member "matrix monotonic optimization for mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hybrid transceiver optimization for multi hop communications")
  Member "robust transceiver with tomlinson harashima precoding for amplify and forward mimo relaying systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hybrid transceiver optimization for multi hop communications")
  Member "miso capacity with per antenna power constraint":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the capacity and optimal signaling for gaussian mimo channels under interference constraints full version")

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)