human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on coverage probability in uplink noma with instantaneous signal power based user ranking"):
  Abstract: In uplink non-orthogonal multiple access (NOMA) networks, the order of decoding at the base station (BS) depends on the received instantaneous signal powers (ISPs). Therefore, the signal-to-interference-plus-noise ratio (SINR) coverage probability for an uplink NOMA user should be calculated using the ISP-based ranking of the users. In the existing literature, however, mean signal power (MSP)-based ranking is used to determine the decoding order and calculate the SINR coverage probability. Although this approximation provides tractable results, it is not accurate. In this letter, we derive the coverage probability for ISP-based ranking, and we show that MSP-based ranking underestimates the SINR coverage probability.

Includes members:
  Member "accuracy of distance based ranking of users in the analysis of noma systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "downlink analysis of noma enabled cellular networks with 3gpp inspired user ranking")
  Member "user point processes in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scheduling policies for federated learning in wireless networks")
  Member "a general mimo framework for noma downlink and uplink transmission based on signal alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mimo noma networks relying on reconfigurable intelligent surface a signal cancellation based design")

Question: Please tell me which class the source paper 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)