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: "low complexity multiuser qam detection for uplink 1 bit massive mimo"
  Abstract: This letter studies multiuser detection for 1-bit massive multiple-input multiple-output systems in order to diminish the power consumption at the base station (BS). A low-complexity near-maximum-likelihood (nML) multiuser detection algorithm is designed, assuming that each BS antenna port is connected with a pair of single-bit resolution analog-to-digital converters and each user equipment (UE) transmits symbols from a quadrature amplitude modulation constellation. First, a novel convex program is formulated as a convex surrogate of the ML detector and subsequently solved through an accelerated first-order method. Then, the solution of the convex optimization problem is harnessed to solve a refined combinatorial problem with reduced search space, requiring non-exponential complexity on the number of the UEs. Judicious simulation study corroborates the efficacy of the resulting two-phase detection algorithm. The proposed two-phase algorithm can achieve symbol error rate performance close to the ML detector, with significantly reduced computation cost compared with the nML detection schemes in prior art.

Connected through hyperedge HE-1 (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"
    - "a general framework for low resolution receivers for mimo channels"
    - "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"

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)