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: "on weighted mse model for mimo transceiver optimization"
  Abstract: Mean-squared-error (MSE) is one of the most widely used performance metrics for the designs and analysis of multi-input-multiple-output (MIMO) communications. Weighted MSE minimization, a more general formulation of MSE minimization, plays an important role in MIMO transceiver optimization. While this topic has a long history and has been extensively studied, existing treatments on the methods in solving the weighted MSE optimization are more or less sporadic and non-systematic. In this paper, we firstly review the two major methodologies, Lagrange multiplier method and majorization theory based method, and their common procedures in solving the weighted MSE minimization. Then some problems and limitations of the methods that were usually neglected or glossed over in existing literature are provided. These problems are fundamental and of critical importance for the corresponding MIMO transceiver optimizations. In addition, a new extended matrix-field weighted MSE model is proposed. Its solutions and applications are discussed in details. Compared with existing models, this new model has wider applications, e.g., nonlinear MIMO transceiver designs and capacity-maximization transceiver designs for general MIMO networks.

Connected through hyperedge HE-1 (induced by source paper "hybrid transceiver optimization for multi hop communications"):
  with members:
    - "hybrid beamforming for millimeter wave systems using the mmse criterion"
    - "majorization minimization aided hybrid transceivers for mimo interference channels"
    - "hybrid precoding architecture for massive multiuser mimo with dissipation sub connected or fully connected structures"
    - "hybrid beamforming with selection for multiuser massive mimo systems"
    - "spectral efficiency optimization for millimeter wave multiuser mimo systems"
    - "spectral and energy efficiency of multi pair massive mimo relay network with hybrid processing"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "unified overview of matrix monotonic optimization for mimo transceivers"):
  with members:
    - "a framework for joint design of pilot sequence and linear precoder"
    - "a framework for transceiver designs for multi hop communications with covariance shaping constraints"
    - "fifty years of mimo detection the road to large scale mimos"
    - "convex separable problems with linear constraints in signal processing and communications"
    - "matrix monotonic optimization for mimo systems"
    - "robust transceiver with tomlinson harashima precoding for amplify and forward mimo relaying systems"
    - "miso capacity with per antenna power constraint"

Connected through hyperedge HE-3 (induced by source paper "unified framework of kkt conditions based matrix optimizations for mimo communications"):
  with members:
    - "transceiver designs with matrix version water filling architecture under mixed power constraints"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "mimo b mac interference network optimization under rate constraints by polite water filling and duality"
    - "miso capacity with per antenna power constraint"

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)