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 "iterative receivers for downlink mimo scma message passing and distributed cooperative detection"):
  Abstract: The rapid development of the mobile communications requires ever higher spectral efficiency. The non-orthogonal multiple access (NOMA) has emerged as a promising technology to further increase the access efficiency of wireless networks. Amongst several NOMA schemes, the sparse code multiple access (SCMA) has been shown to be able to achieve better performance. In this paper, we consider a downlink MIMO-SCMA system over frequency selective fading channels. For optimal detection, the complexity increases exponentially with the product of the number of users, the number of antennas and the channel length. To tackle this challenge, we propose near optimal low-complexity iterative receivers based on factor graph. By introducing auxiliary variables, a stretched factor graph is constructed and a hybrid belief propagation (BP) and expectation propagation (EP) receiver, named as `Stretch-BP-EP', is proposed. Considering the convergence problem of BP algorithm on loopy factor graph, we convexify the Bethe free energy and propose a convergence-guaranteed BP-EP receiver, named as `Conv-BP-EP'. We further consider cooperative network and propose two distributed cooperative detection schemes to exploit the diversity gain, namely, belief consensus-based algorithm and Bregman alternative direction method of multipliers (ADMM)-based method. Simulation results verify the superior performance of the proposed Conv-BP-EP receiver compared with other methods. The two proposed distributed cooperative detection schemes can improve the bit error rate performance by exploiting the diversity gain. Moreover, Bregman ADMM method outperforms the belief consensus-based algorithm in noisy inter-user links.

Includes members:
  Member "wireless powered communications with non orthogonal multiple access":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interplay between noma and other emerging technologies a survey")
  Member "nonorthogonal multiple access in large scale underlay cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resource allocation for irs assisted full duplex cognitive radio systems")
  Member "a survey of downlink non orthogonal multiple access for 5g wireless communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "random access for massive machine type communications")
  Member "gaussian message passing iterative detection for mimo noma systems with massive access":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior")
  Member "fast admm algorithm for distributed optimization with adaptive penalty":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithms and software for projections onto intersections of convex and non convex sets with applications to inverse problems")
  Member "scma codebook design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "design of scma codebooks using differential evolution")
  Member "what will 5g be":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "performance analysis of intelligent reflecting surface assisted noma networks")

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)