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: "capacity of the discrete time awgn channel under output quantization"
  Abstract: We investigate the limits of communication over the discrete-time Additive White Gaussian Noise (AWGN) channel, when the channel output is quantized using a small number of bits. We first provide a proof of our recent conjecture on the optimality of a discrete input distribution in this scenario. Specifically, we show that for any given output quantizer choice with K quantization bins (i.e., a precision of log2 K bits), the input distribution, under an average power constraint, need not have any more than K + 1 mass points to achieve the channel capacity. The cutting-plane algorithm is employed to compute this capacity and to generate optimum input distributions. Numerical optimization over the choice of the quantizer is then performed (for 2-bit and 3-bit symmetric quantization), and the results we obtain show that the loss due to low-precision output quantization, which is small at low signal-to-noise ratio (SNR) as expected, can be quite acceptable even for moderate to high SNR values. For example, at SNRs up to 20 dB, 2-3 bit quantization achieves 80-90% of the capacity achievable using infinite-precision quantization.

Connected through hyperedge HE-1 (induced by source paper "new equivalent model of quantizer with noisy input and its application for adc resolution determination in an uplink mimo receiver"):
  with members:
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "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"
    - "minimum ber precoding in 1 bit massive mimo systems"
    - "on the spectral efficiency of massive mimo systems with low resolution adcs"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "quantized precoding for massive mu mimo"

Connected through hyperedge HE-2 (induced by source paper "low snr asymptotic rates of vector channels with one bit outputs"):
  with members:
    - "gaussian multiple access channels with one bit quantizer at the receiver"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "how much training is needed in one bit massive mimo systems at low snr"
    - "mixed adc massive mimo"
    - "channel estimation and uplink achievable rates in one bit massive mimo systems"
    - "one bit massive mimo channel estimation and high order modulations"

Connected through hyperedge HE-3 (induced by source paper "oblivious fronthaul constrained relay for a gaussian channel"):
  with members:
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "oblivious processing in a fronthaul constrained gaussian channel"
    - "remarks on the most informative function conjecture at fixed mean"
    - "lower bounds and approximations for the information rate of the isi channel"
    - "on the capacity of the dither quantized gaussian channel"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "distributed mimo receiver achievable rates and upper bounds"

Connected through hyperedge HE-4 (induced by source paper "phase quantized block noncoherent communication"):
  with members:
    - "a general framework for transmission with transceiver distortion and some applications"
    - "asymmetric quantizers are better at low snr"
    - "towards a communication theoretic understanding of system level power consumption"
    - "on block noncoherent communication with low precision phase quantization at the receiver"

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)