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 "compressive sensing based multiuser detector for the large scale sm mimo uplink"):
  Abstract: Conventional spatial modulation (SM) is typically considered for transmission in the downlink of small-scale multiple-input–multiple-output (MIMO) systems, where a single antenna element (AE) of a set of, e.g.,   $\mbox{2}^p$   AEs is activated for implicitly conveying   $p$   bits. By contrast, inspired by the compelling benefits of large-scale MIMO (LS-MIMO) systems, here, we propose an LS-SM-MIMO scheme for the uplink (UL), where each user having multiple AEs but only a single radio frequency (RF) chain invokes SM for increasing the UL throughput. At the same time, by relying on hundreds of AEs and a small number of RF chains, the base station (BS) can simultaneously serve multiple users while reducing the power consumption. Due to the large number of AEs of the UL users and the comparably small number of RF chains at the BS, the UL multiuser signal detection becomes a challenging large-scale underdetermined problem. To solve this problem, we propose a joint SM transmission scheme and a carefully designed structured compressive sensing (SCS)-based multiuser detector (MUD) to be used at the users and the BS, respectively. Additionally, the cyclic-prefix single carrier (CPSC) is used to combat the multipath channels, and a simple receive AE selection is used for the improved performance over correlated Rayleigh-fading MIMO channels. We demonstrate that the aggregate SM signal consisting of multiple UL users' SM signals of a CPSC block exhibits distributed sparsity. Moreover, due to the joint SM transmission scheme, aggregate SM signals in the same transmission group exhibit group sparsity. By exploiting these intrinsically sparse features, the proposed SCS-based MUD can reliably detect the resultant SM signals with low complexity. Simulation results demonstrate that the proposed SCS-based MUD achieves a better signal detection performance than its counterparts even with higher UL throughput.

Includes members:
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "practical implementation of spatial modulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on spatial modulation in emerging wireless systems research progresses and applications")
  Member "generalised sphere decoding for spatial modulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on spatial modulation in emerging wireless systems research progresses and applications")
  Member "structured compressed sensing from theory to applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive sensing for cut improvement and local clustering")

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)