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: "spectral efficiency of mixed adc massive mimo"
  Abstract: We study the spectral efficiency (SE) of a mixed-analog-to-digital converter (ADC) massive multiple-input multiple-output (MIMO) system in which   $K$   single-antenna users communicate with a base station equipped with   $M$    antennas connected to   $N$    high-resolution ADCs and   $M-N$   1-b ADCs. This architecture has been proposed as an approach for realizing massive MIMO systems with reasonable power consumption. First, we investigate the effectiveness of mixed-ADC architectures in overcoming the channel estimation error caused by coarse quantization. For the channel estimation phase, we study to what extent one can combat the SE loss by exploiting just   $N\ll M$   pairs of high-resolution ADCs. We extend the round-robin training scheme for mixed-ADC systems to include both high-resolution and 1-b quantized observations. Then, we analyze the impact of the resulting channel estimation error in the data detection phase. We consider random high-resolution ADC assignment and also analyze a simple antenna selection scheme to increase the SE. Analytical expressions are derived for the SE for maximum ratio combining and numerical results are presented for zero-forcing detection. Performance comparisons are made against systems with uniform ADC resolution and against mixed-ADC systems without round-robin training to illustrate under what conditions each approach provides the greatest benefit.

Connected through hyperedge HE-1 (induced by source paper "task based quantization with application to mimo receivers"):
  with members:
    - "dynamic metasurface antennas for mimo ofdm receivers with bit limited adcs"
    - "dynamic metasurface antennas for uplink massive mimo systems"
    - "hardware limited task based quantization"
    - "on the spectral efficiency of noncooperative uplink massive mimo systems"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "principal inertia components and applications"

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"
    - "multipair massive mimo relaying systems with one bit adc s and dac s"
    - "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"

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)