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: "multipair massive mimo relaying systems with one bit adc s and dac s"
  Abstract: This paper considers a multipair amplify-and-forward massive multipair multiple-input multiple-output relaying system with one-bit analog-to-digital converters and one-bit digital-to-analog converters at the relay. The channel state information is estimated via pilot training, and then utilized by the relay to perform simple maximum-ratio combining/maximum-ratio transmission processing. Leveraging on the Bussgang decomposition, an exact achievable rate is derived for the system with correlated quantization noise. Based on this, a closed-form asymptotic approximation for the achievable rate is presented, thereby enabling efficient evaluation of the impact of key parameters on the system performance. Furthermore, power scaling laws are characterized to study the potential energy efficiency associated with deploying massive one-bit antenna arrays at the relay. In addition, a power allocation strategy is designed to compensate for the rate degradation caused by the coarse quantization. Our results suggest that the quality of the channel estimates depends on the specific orthogonal pilot sequences that are used, contrary to unquantized systems where any set of orthogonal pilot sequences gives the same result. Moreover, the sum rate gap between the double-quantized relay system and an ideal nonquantized system is a moderate factor of    ${\text{4}}/\pi ^2$   in the low power regime.

Connected through hyperedge HE-1 (induced by source paper "supervised learning for multi hop mu mimo communications with one bit transceivers"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "one bit sphere decoding for uplink massive mimo systems with one bit adcs"
    - "an introduction to deep learning for the physical layer"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "detection algorithms for communication systems using deep learning"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "quantized massive mu mimo ofdm uplink"

Connected through hyperedge HE-2 (induced by source paper "mixed adc dac multipair massive mimo relaying systems performance analysis and power optimization"):
  with members:
    - "all technologies work together for good a glance to future mobile networks"
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "spectral efficiency of mixed adc massive mimo"
    - "on performance of quantized transceiver in multiuser massive mimo downlinks"
    - "the distributed mimo scenario can ideal adcs be replaced by low resolution adcs"
    - "spectral efficiency of multipair massive mimo two way relaying with hardware impairments"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "on the total energy efficiency of cell free massive mimo"

Connected through hyperedge HE-3 (induced by source paper "finite alphabet precoding for massive mu mimo with low resolution dacs"):
  with members:
    - "energy efficiency of mmwave massive mimo precoding with low resolution dacs"
    - "1 bit massive mu mimo precoding in vlsi"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "full duplex massive mimo relaying systems with low resolution adcs"
    - "massive mu mimo ofdm downlink with one bit dacs and linear precoding"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "mixed adc massive mimo"

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)