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: "generalized spatial modulation in large scale multiuser mimo systems"
  Abstract: Generalized spatial modulation (GSM) uses    $n_{t} $   transmit antenna elements but fewer transmit radio frequency (RF) chains,    $n_{rf} $  . Spatial modulation (SM) and spatial multiplexing are special cases of GSM with    $n_{rf}=1$   and    $n_{rf}=n_{t} $  , respectively. In GSM, in addition to conveying information bits through    $n_{rf} $   conventional modulation symbols (for example, QAM), the indices of the    $n_{rf} $   active transmit antennas also convey information bits. In this paper, we investigate GSM for large-scale multiuser MIMO communications on the uplink. Our contributions in this paper include: 1) an average bit error probability (ABEP) analysis for maximum-likelihood detection in multiuser GSM-MIMO on the uplink, where we derive an upper bound on the ABEP, and 2) low-complexity algorithms for GSM-MIMO signal detection and channel estimation at the base station receiver based on message passing. The analytical upper bounds on the ABEP are found to be tight at moderate to high signal-to-noise ratios (SNR) . The proposed receiver algorithms are found to scale very well in complexity while achieving near-optimal performance in large dimensions. Simulation results show that, for the same spectral efficiency, multiuser GSM-MIMO can outperform multiuser SM-MIMO as well as conventional multiuser MIMO, by about 2 to 9 dB at a bit error rate of    $10^{-3} $  . Such SNR gains in GSM-MIMO compared to SM-MIMO and conventional MIMO can be attributed to the fact that, because of a larger number of spatial index bits, GSM-MIMO can use a lower-order QAM alphabet which is more power efficient.

Connected through hyperedge HE-1 (induced by source paper "double sparsity learning based channel and signal estimation in massive mimo with generalized spatial modulation"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "super resolution blind channel and signal estimation for massive mimo with arbitrary array geometry"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "parametric bilinear generalized approximate message passing"
    - "bilinear generalized approximate message passing part i derivation"
    - "optimal design of energy efficient multi user mimo systems is massive mimo the answer"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-2 (induced by source paper "spatial coded modulation"):
  with members:
    - "what will 5g be"
    - "generalised sphere decoding for spatial modulation"

Connected through hyperedge HE-3 (induced by source paper "transmit receive generalized spatial modulation based on dual layered mimo transmission"):
  with members:
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"

Connected through hyperedge HE-4 (induced by source paper "multidimensional index modulation in wireless communications"):
  with members:
    - "index modulation techniques for 5g wireless networks"
    - "on multiple input multiple output ofdm with index modulation for next generation wireless networks"
    - "on media based modulation using rf mirrors"
    - "on the capacity and performance of generalized spatial modulation"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "spectral efficiency analysis for spatial modulation in massive mimo uplink over dispersive channels"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "low complexity hybrid precoding in massive multiuser mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-6 (induced by source paper "multiuser media based modulation for massive mimo systems"):
  with members:
    - "on media based modulation using rf mirrors"
    - "media based mimo a new frontier in wireless communications"
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "space time index modulation"):
  with members:
    - "generalized space and frequency index modulation"

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)