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: "optimal feedback rate sharing strategy in zero forcing mimo broadcast channels"
  Abstract: In this paper, we consider a multiple-input multiple-output broadcast channel with limited feedback where all users share the feedback rates. Firstly, we find the optimal feedback rate sharing strategy using zero-forcing transmission scheme at the transmitter and random vector quantization at each user. We mathematically prove that equal sharing of sum feedback size among all users is the optimal strategy in the low signal-to-noise ratio (SNR) region, while allocating whole feedback size to a single user is the optimal strategy in the high SNR region. For the mid-SNR region, we propose a simple numerical method to find the optimal feedback rate sharing strategy based on our analysis and show that the equal allocation of sum feedback rate to a partial number of users is the optimal strategy. It is also shown that the proposed simple numerical method can be applicable to finding the optimal feedback rate sharing strategy when different path losses of the users are taken into account. Our proposed feedback rate sharing scheme can be extended to the system with stream control and is still useful for the systems with other techniques such as regularized zero-forcing and spherical cap codebook.

Connected through hyperedge HE-1 (induced by source paper "trellis extended codebooks and successive phase adjustment a path from lte advanced to fdd massive mimo systems"):
  with members:
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "bounds on eigenvalues of a spatial correlation matrix"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "capacity of a multiple antenna fading channel with a quantized precoding matrix"
    - "mimo broadcast channels with finite rate feedback"
    - "limited feedback beamforming over temporally correlated channels"

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)