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: "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.

Connected through hyperedge HE-1 (induced by source paper "a survey on spatial modulation in emerging wireless systems research progresses and applications"):
  with members:
    - "spatial modulation assisted multi antenna non orthogonal multiple access"
    - "adaptive spatial modulation based visible light communications ser analysis and optimization"
    - "two high performance schemes of transmit antenna selection for secure spatial modulation"
    - "receive spatial modulation for massive mimo systems"
    - "polarization shift keying polarsk system scheme and performance analysis"
    - "space time channel modulation"
    - "on the achievable spectral efficiency of spatial modulation aided downlink non orthogonal multiple access"
    - "spectral efficient analog precoding for generalized spatial modulation aided mmwave mimo"

Connected through hyperedge HE-2 (induced by source paper "a novel error performance analysis methodology for ofdm im"):
  with members:
    - "index modulation for 5g striving to do more with less"
    - "index modulation techniques for 5g wireless networks"

Connected through hyperedge HE-3 (induced by source paper "grant free massive mtc enabled massive mimo a compressive sensing approach"):
  with members:
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a novel receiver design with joint coherent and non coherent processing"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random pilot and data access in massive mimo for machine type communications"
    - "random access protocols for massive mimo"
    - "design and performance analysis of noncoherent detection systems with massive receiver arrays"
    - "massive machine type communications in 5g physical and mac layer solutions"

Connected through hyperedge HE-4 (induced by source paper "sparse representation for wireless communications"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "common codebook millimeter wave beam design designing beams for both sounding and communication with uniform planar arrays"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "compressed channel feedback for correlated massive mimo systems"
    - "compressive sensing based differential channel feedback for massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-5 (induced by source paper "device activity and embedded information bit detection using amp in massive mimo"):
  with members:
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "random access protocols for massive mimo"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "belief propagation for joint sparse recovery"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "compressive sensing techniques for next generation wireless communications"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"

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)