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: "spectrum sharing opportunities of full duplex systems using improper gaussian signaling"
  Abstract: Sharing the licensed spectrum of full-duplex (FD) primary users (PU) brings strict limitations on the underlay cognitive radio operation. Particularly, the self interference may overwhelm the PU receiver and limit the opportunity of secondary users (SU) to access the spectrum. Improper Gaussian signaling (IGS) has demonstrated its superiority in improving the performance of interference channel systems. Throughout this paper, we assume a FD PU pair that uses proper Gaussian signaling (PGS), and a half-duplex SU pair that uses IGS. The objective is to maximize the SU instantaneous achievable rate while meeting the PU quality-of-service. To this end, we propose a simplified algorithm that optimizes the SU signal parameters, i.e, the transmit power and the circularity coefficient, which is a measure of the degree of impropriety of the SU signal, to achieve the design objective. Numerical results show the merits of adopting IGS compared with PGS for the SU especially with the existence of week PU direct channels and/or strong SU interference channels.

Connected through hyperedge HE-1 (induced by source paper "full duplex relaying with improper gaussian signaling over nakagami m fading channels"):
  with members:
    - "rate region boundary of the siso z interference channel with improper signaling"
    - "improper gaussian signaling in full duplex relay channels with residual self interference"
    - "underlay spectrum sharing techniques with in band full duplex systems using improper gaussian signaling"
    - "benefits of improper signaling for underlay cognitive radio"
    - "improper signaling for symbol error rate minimization in k user interference channel"
    - "optimized transmission with improper gaussian signaling in the k user miso interference channel"
    - "transmit optimization with improper gaussian signaling for interference channels"
    - "in band full duplex wireless challenges and opportunities"

Connected through hyperedge HE-2 (induced by source paper "underlay spectrum sharing techniques with in band full duplex systems using improper gaussian signaling"):
  with members:
    - "improper gaussian signaling in full duplex relay channels with residual self interference"
    - "throughput analysis and optimization of wireless powered multiple antenna full duplex relay systems"
    - "sharing the licensed spectrum of full duplex systems using improper gaussian signaling"
    - "benefits of improper signaling for underlay cognitive radio"
    - "optimized transmission with improper gaussian signaling in the k user miso interference channel"
    - "transmit optimization with improper gaussian signaling for interference channels"
    - "in band full duplex wireless challenges and opportunities"
    - "interference alignment with asymmetric complex signaling settling the host madsen nosratinia conjecture"

Connected through hyperedge HE-3 (induced by source paper "improper gaussian signaling in full duplex relay channels with residual self interference"):
  with members:
    - "sharing the licensed spectrum of full duplex systems using improper gaussian signaling"
    - "transmit optimization with improper gaussian signaling for interference channels"
    - "in band full duplex wireless challenges and opportunities"
    - "experiment driven characterization of full duplex wireless systems"
    - "interference alignment with asymmetric complex signaling settling the host madsen nosratinia conjecture"

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)