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: "quantized precoding for multi antenna downlink channels with magiq"
  Abstract: A multi-antenna, greedy, iterative, and quantized (MAGIQ) precoding algorithm is proposed for downlink channels. MAGIQ allows a straightforward integration with orthogonal frequency-division multiplexing (OFDM). MAGIQ is compared to three existing algorithms in terms of information rates and complexity: quantized linear precoding (QLP), SQUID, and an ADMM-based algorithm. The information rate is measured by using a lower bound for finite modulation sets, and the complexity is measured by the number of multiplications and comparisons. MAGIQ and ADMM achieve similar information rates with similar complexity for Rayleigh flat-fading channels and one-bit quantization per real dimension, and they outperform QLP and SQUID for higher order modulation.

Connected through hyperedge HE-1 (induced by source paper "massive mu mimo ofdm uplink with direct rf sampling and 1 bit adcs"):
  with members:
    - "timing and frequency synchronization for 1 bit massive mu mimo ofdm downlink"
    - "massive mu mimo ofdm uplink with hardware impairments modeling and analysis"
    - "one bit massive mimo precoding via a minimum symbol error probability design"
    - "directional frame timing synchronization in wideband millimeter wave systems with low resolution adcs"
    - "one bit sphere decoding for uplink massive mimo systems with one bit adcs"
    - "performance analysis for time of arrival estimation with oversampled low complexity 1 bit a d conversion"
    - "nonlinear precoding for phase quantized constant envelope massive mu mimo ofdm"
    - "on out of band emissions of quantized precoding in massive mu mimo ofdm"

Connected through hyperedge HE-2 (induced by source paper "neural network optimized 1 bit precoding for massive mu mimo"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "multilevel mimo detection with deep learning"
    - "one bit massive mimo precoding via a minimum symbol error probability design"
    - "quantized constant envelope precoding with psk and qam signaling"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "a model driven deep learning network for mimo detection"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "1 bit massive mu mimo precoding in vlsi"

Connected through hyperedge HE-3 (induced by source paper "timing and frequency synchronization for 1 bit massive mu mimo ofdm downlink"):
  with members:
    - "directional frame timing synchronization in wideband millimeter wave systems with low resolution adcs"
    - "one bit precoding and constellation range design for massive mimo with qam signaling"
    - "1 bit massive mu mimo precoding in vlsi"
    - "performance analysis for time of arrival estimation with oversampled low complexity 1 bit a d conversion"
    - "nonlinear precoding for phase quantized constant envelope massive mu mimo ofdm"
    - "joint cfo and channel estimation in millimeter wave systems with one bit adcs"
    - "linear precoding with low resolution dacs for massive mu mimo ofdm downlink"
    - "downlink achievable rate analysis in massive mimo systems with one bit dacs"

Connected through hyperedge HE-4 (induced by source paper "quantized constant envelope precoding with psk and qam signaling"):
  with members:
    - "1 bit massive mu mimo precoding in vlsi"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "nonlinear precoding for phase quantized constant envelope massive mu mimo ofdm"
    - "linear precoding with low resolution dacs for massive mu mimo ofdm downlink"
    - "psk precoding in multi user miso systems"
    - "massive mimo downlink 1 bit precoding with linear programming for psk signaling"
    - "an efficient manifold algorithm for constructive interference based constant envelope precoding"
    - "interference exploitation in full duplex communications trading interference power for both uplink and downlink power savings"

Connected through hyperedge HE-5 (induced by source paper "linear precoding with low resolution dacs for massive mu mimo ofdm downlink"):
  with members:
    - "one bit massive mimo precoding via a minimum symbol error probability design"
    - "a framework for one bit and constant envelope precoding over multiuser massive miso channels"
    - "mse optimal 1 bit precoding for multiuser mimo via branch and bound"
    - "one bit precoding and constellation range design for massive mimo with qam signaling"
    - "can hardware distortion correlation be neglected when analyzing uplink se in massive mimo"
    - "out of band radiation from antenna arrays clarified"
    - "1 bit massive mu mimo precoding in vlsi"
    - "throughput analysis of massive mimo uplink with low resolution adcs"

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)