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 the performance gain of noma over oma in uplink communication systems"):
  Abstract: In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by $\gamma$ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to $M$-antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of $M$ for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, $M$, and the number of users, $K$, the ESG of NOMA over OMA increases linearly with both $M$ and $K$. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

Includes members:
  Member "a two stage beam alignment framework for hybrid mmwave distributed antenna systems":
    also connected through other hyperedges:
      (none)
  Member "on the performance gain of noma over oma in uplink single cell systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "random access for massive machine type communications")
  Member "a distributed multi rf chain hybrid mmwave scheme for small cell systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a two stage beam alignment framework for hybrid mmwave distributed antenna systems")
  Member "multi beam noma for hybrid mmwave systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint trajectory and resource allocation design for energy efficient secure uav communication systems")
  Member "joint pilot and payload power control for uplink mimo noma with mrc sic receivers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random access for massive machine type communications")
  Member "multi cell hybrid millimeter wave systems pilot contamination and interference mitigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a distributed multi rf chain hybrid mmwave scheme for small cell systems")
  Member "noma aided narrowband iot for machine type communications with user clustering":
    also connected through other hyperedges:
      (none)
  Member "non orthogonal multiple access common myths and critical questions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cognitive radio networks an information theoretic perspective")

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)