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: "bayesian optimal data detector for mmwave ofdm system with low resolution adc"
  Abstract: Orthogonal frequency division multiplexing (OFDM) has been widely used in communication systems operating in the millimeter wave (mmWave) band to combat frequency-selective fading and achieve multi-Gbps transmissions, such as the IEEE 802.15.3c and the IEEE 802.11ad. For mmWave systems with ultra high sampling rate requirements, the use of low-resolution analog-to-digital converters (ADCs) (i.e., 1–3 bits) ensures an acceptable level of power consumption and system costs. However, orthogonality among subchannels in the OFDM system cannot be maintained because of the severe nonlinearity caused by low-resolution ADC, which renders the design of data detector challenging. In this paper, we develop an efficient algorithm for optimal data detection in the mmWave OFDM system with low-resolution ADCs. The analytical performance of the proposed detector is derived and verified to achieve the fundamental limit of the Bayesian optimal design. On the basis of the derived analytical expression, we further propose a power allocation (PA) scheme that seeks to minimize the average symbol error rate. In addition to the optimal data detector, we also develop a feasible channel estimation method, which can provide high-quality channel state information without significant pilot overhead. Simulation results confirm the accuracy of our analysis and illustrate that the performance of the proposed detector in conjunction with the proposed PA scheme is close to the optimal performance of the OFDM system with infinite-resolution ADC.

Connected through hyperedge HE-1 (induced by source paper "time varying downlink channel tracking for quantized massive mimo networks"):
  with members:
    - "beamspace aware adaptive channel estimation for single carrier time varying massive mimo channels"
    - "interference alignment and soft space reuse based cooperative transmission for multi cell massive mimo networks"
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"
    - "two tier precoding for fdd multi cell massive mimo time varying interference networks"

Connected through hyperedge HE-2 (induced by source paper "bayesian optimal data detector for hybrid mmwave mimo ofdm systems with low resolution adcs"):
  with members:
    - "resolution adaptive hybrid mimo architectures for millimeter wave communications"
    - "performance analysis of mixed adc massive mimo systems over rician fading channels"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "mixed adc massive mimo"
    - "mixed adc massive mimo uplink in frequency selective channels"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-3 (induced by source paper "performance analysis of multi cell millimeter wave massive mimo networks with low precision adcs"):
  with members:
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "a hardware efficient analog network structure for hybrid precoding in millimeter wave systems"
    - "a comparison of hybrid beamforming and digital beamforming with low resolution adcs for multiple users and imperfect csi"
    - "secure massive mimo systems with limited rf chains"
    - "on performance of quantized transceiver in multiuser massive mimo downlinks"
    - "spectral and energy efficiency of multi pair massive mimo relay network with hybrid processing"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "base station selection for massive mimo networks with two stage precoding"

Connected through hyperedge HE-4 (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"
    - "a general framework for low resolution receivers for mimo channels"
    - "uplink multiuser massive mimo systems with low resolution adcs a coding theoretic approach"
    - "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-5 (induced by source paper "one bit ofdm receivers via deep learning"):
  with members:
    - "ofdm autoencoder for end to end learning of communications systems"
    - "an introduction to deep learning for the physical layer"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "detection algorithms for communication systems using deep learning"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "mixed adc massive mimo"

Connected through hyperedge HE-6 (induced by source paper "soft output detection methods for sparse millimeter wave mimo systems with low precision adcs"):
  with members:
    - "joint channel estimation decoding with frequency selective channels and few bit adcs"
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "bayesian optimal data detector for hybrid mmwave mimo ofdm systems with low resolution adcs"
    - "one bit sphere decoding for uplink massive mimo systems with one bit adcs"
    - "performance analysis of mixed adc massive mimo systems over rician fading channels"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "on the spectral efficiency of massive mimo systems with low resolution adcs"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"

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)