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: "user selection and widely linear multiuser precoding for one dimensional signalling"
  Abstract: Massive deployment of low data rate Internet of things and ehealth devices prompts us to develop more practical precoding and user selection techniques that comply with these requirements. Moreover, it is known that when the data is real-valued and the observation is complex-valued, widely linear (WL) estimation can be employed in lieu of linear estimation to improve the performance. With these motivations, in this paper, we study the transmit precoding (beamforming) in multiuser multiple-input single-output communications systems assuming the transmit signal is one-dimensionally modulated and widely linear estimation is performed at the receivers. Closed-form solutions for widely linear maximum ratio transmission (MRT), WL zero-forcing (ZF), WL minimum mean square error (MMSE), and WL maximum signal to leakage and noise ratio (MSLNR) precoding are obtained. It is shown that widely linear processing can potentially double the number of simultaneous users compared to the linear processing of one-dimensionally modulated signals. Furthermore, to deal with the increasing number of communications devices a user selection algorithm compatible with widely linear processing of one-dimensionally modulated signals is proposed. The proposed user selection algorithm can double the number of simultaneously selected users compared to conventional user selection methods.

Connected through hyperedge HE-1 (induced by source paper "blind receive beamforming for autonomous grant free high overloading multiple access"):
  with members:
    - "blind multi user detection for autonomous grant free high overloading ma without reference signal"
    - "maximum signal minus interference to noise ratio multiuser receive beamforming"

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)