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: "channel pre inversion and max sinr vector perturbation for large scale broadcast channels"
  Abstract: We study channel pre-inversion and vector perturbation (VP) schemes for large-scale broadcast channels, wherein a transmitter has $M$ transmit antennas and is transmitting to $K$ single-antenna non-cooperating receivers. We provide results which predict the capacity of MMSE pre-inversion as $K\rightarrow\infty$. We construct a new VP strategy, max-SINR vector perturbation (MSVP), which maximizes a sharp estimate of the signal-to-interference-plus-noise ratio. We provide results which predict the performance of MSVP and demonstrate that MSVP outperforms other VP methods. Lastly, we combine MSVP with the low-complexity Sorted QR Precoding method to show that MSVP has the potential to efficiently deliver data to a very large number of users at close to channel capacity.

Connected through hyperedge HE-1 (induced by source paper "hybrid vector perturbation precoding the blessing of approximate message passing"):
  with members:
    - "boosted kz and lll algorithms"
    - "hybrid approximate message passing"
    - "on the performance of mismatched data detection in large mimo systems"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "optimality of large mimo detection via approximate message passing"
    - "massive mimo for next generation wireless systems"
    - "expectation propagation for approximate bayesian inference"
    - "proximity factors of lattice reduction aided precoding for multiantenna broadcast"

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)