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: "monobit digital receivers for qpsk design analysis and performance"
  Abstract: Future communication system requires large bandwidth to achieve high data rate up to multigigabit/ sec, which makes analog-to-digital (ADC) become a key bottleneck for the implementation of digital receivers due to its high complexity and large power consumption. Therefore, monobit receivers for BPSK have been proposed to address this problem. In this work, QPSK modulation is considered for higher data rate. First, the optimal receiver based on monobit ADC with Nyquist sampling is derived, and its corresponding performance in the form of deflection ratio is calculated. Then a suboptimal but more practical monobit receiver is obtained, along with iterative demodulation and small sample removal. The effect of the imbalances between the In-phase (I) and Quadrature-phase (Q) branches, including the amplitude and phase imbalances, is carefully investigated too. To combat the performance loss caused by IQ imbalances, monobit receivers based on double training sequences are proposed. Numerical simulations show that the low-complexity suboptimal receiver suffers only 3dB signal to noise ratio (SNR) loss in AWGN channels and 1dB SNR loss in multipath static channels compared with the matched filter based monobit receiver with full channel state information (CSI). The impact of the phase difference between the transmitter and receiver is presented. It is observed that the performance degradation caused by the amplitude imbalance is negligible. Receivers based on double training sequences can efficiently compensate the performance loss in AWGN channel. Thanks to the diversity offered by the multipath, the effect of imbalances on monobit receivers in fading channels is slight. I

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)