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: "performance analysis of noma uplink networks under statistical qos delay constraints"
  Abstract: In the fifth generation and beyond (B5G), delayconstraints emerge as a topic of particular interest, e.g. forultra-reliable low latency communications (URLLC) such asautonomous vehicles and enhanced reality. In this paper, westudythe performance of a two-user uplink NOMA network understatistical quality of service (QoS) delay constraints, capturedthrough each user s effective capacity (EC). We propose novelclosed-form expressions for the EC of the NOMA users andshow that in the high signal to noise ratio (SNR) region, the 'strong' NOMA user has a limited EC, assuming the same delayconstraint as the 'weak' user. We demonstrate that for the weakuser, OMA achieves higher EC than NOMA at small values ofthe transmit SNR, while NOMA outperforms OMA in terms ofEC at high SNRs. On the other hand, for the strong user theopposite is true, i.e., NOMA achieves higher EC than OMA atsmall SNRs, while OMA becomes more beneficial at high SNRs.This result raises the question of introducing 'adaptive' OMA /NOMA policies, based jointly on the users delay constraints aswell as on the available transmit power.

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)