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: "a general framework for low resolution receivers for mimo channels"
  Abstract: The capacity of a discrete-time multi-input multioutput (MIMO) Gaussian channel with output quantization is investigated for different analog receiver architectures. A general formulation of this problem is proposed in which the antenna outputs are processed by analog combiners while sign quantizers are used for analog/digital conversion. The configuration of the analog combiners is chosen as a function of the channel realization, so that capacity can be maximized over the set of available configurations for a fixed number of sign quantizers. To exemplify this approach, three analog receiver architectures of increasing generality are considered: (a) sign quantization of the antenna outputs, (b) antenna selection and multilevel quantization and (c) linear combining of the antenna outputs and multilevel quantization. By comparing the largest attainable rates for a given number of sign quantizers, it is possible to quantify the limiting rate advantages provided by each of the receiver analog architectures. In particular, it is shown that sign quantization of the antenna outputs is sufficient, among all possible receiver architectures studied, to attain the optimal high signal-to-noise ratio (SNR) performance for MIMO receiver in which the number of antennas is larger than the number of sign quantizers.

Connected through hyperedge HE-1 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints"):
  with members:
    - "tradeoff between delay and high snr capacity in quantized mimo systems"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "on mimo channel capacity with output quantization constraints"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "uplink performance of wideband massive mimo with one bit adcs"

Connected through hyperedge HE-2 (induced by source paper "on the capacity of massive mimo with 1 bit adcs and dacs at the receiver and at the transmitter"):
  with members:
    - "on mimo channel capacity with output quantization constraints"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "massive mimo with 1 bit adc"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "analysis of 1 bit output noncoherent fading channels in the low snr regime"

Connected through hyperedge HE-3 (induced by source paper "reliable ofdm transmission with ultra low resolution adc"):
  with members:
    - "joint channel estimation decoding with frequency selective channels and few bit adcs"
    - "low complexity multiuser qam detection for uplink 1 bit massive mimo"
    - "uplink multiuser massive mimo systems with low resolution adcs a coding theoretic approach"
    - "bayesian optimal data detector for mmwave ofdm system with low resolution adc"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "denoising based turbo compressed sensing"
    - "channel estimation and data equalization in frequency selective mimo systems with one bit quantization"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"

Connected through hyperedge HE-4 (induced by source paper "on mimo channel capacity with output quantization constraints"):
  with members:
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"

Connected through hyperedge HE-5 (induced by source paper "limited feedback in single and multi user mimo systems with finite bit adcs"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "limited feedback in miso systems with finite bit adcs"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "one bit massive mimo channel estimation and high order modulations"
    - "limited feedback in multiple antenna systems with one bit quantization"

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)