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: "joint spatial division and multiplexing"
  Abstract: We propose Joint Spatial Division and Multiplexing (JSDM), an approach to multiuser MIMO downlink that exploits the structure of the correlation of the channel vectors in order to allow for a large number of antennas at the base station while requiring reduced-dimensional Channel State Information at the Transmitter (CSIT). This allows for significant savings both in the downlink training and in the CSIT feedback from the user terminals to the base station, thus making the use of a large number of base station antennas potentially suitable also for Frequency Division Duplexing (FDD) systems, for which uplink/downlink channel reciprocity cannot be exploited. JSDM forms the multiuser MIMO downlink precoder by concatenating a pre-beamforming matrix, which depends only on the channel second-order statistics, with a classical multiuser precoder, based on the instantaneous knowledge of the resulting reduced dimensional effective channels. We prove a simple condition under which JSDM incurs no loss of optimality with respect to the full CSIT case. For linear uniformly spaced arrays, we show that such condition is closely approached when the number of antennas is large. For this case, we use Szego asymptotic theory of large Toeplitz matrices to design a DFT-based pre-beamforming scheme requiring only coarse information about the users angles of arrival and angular spread. Finally, we extend these ideas to the case of a two-dimensional base station antenna array, with 3-dimensional beamforming, including multiple beams in the elevation angle direction. We provide guidelines for the pre-beamforming optimization and calculate the system spectral efficiency under proportional fairness and maxmin fairness criteria, showing extremely attractive performance. Our numerical results are obtained via an asymptotic random matrix theory tool known as deterministic equivalent approximation.

Connected through hyperedge HE-1 (induced by source paper "low complexity hybrid linear tomlinson harashima precoding for downlink large scale mu mimo systems"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-2 (induced by source paper "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"):
  with members:
    - "uplink performance of time reversal mrc in massive mimo systems subject to phase noise"
    - "robust estimates of covariance matrices in the large dimensional regime"
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"
    - "blind pilot decontamination"
    - "massive mimo and small cells improving energy efficiency by optimal soft cell coordination"
    - "capacity limits and multiplexing gains of mimo channels with transceiver impairments"
    - "hardware impairments in large scale miso systems energy efficiency estimation and capacity limits"
    - "linear precoding based on polynomial expansion reducing complexity in massive mimo"

Connected through hyperedge HE-3 (induced by source paper "linear precoding based on polynomial expansion large scale multi cell mimo systems"):
  with members:
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "linear precoding based on polynomial expansion reducing complexity in massive mimo"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "pareto characterization of the multicell mimo performance region with simple receivers"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "asymptotic moments for interference mitigation in correlated fading channels"
    - "pilot contamination and precoding in multi cell tdd systems"
    - "a central limit theorem for the sinr at the lmmse estimator output for large dimensional signals"

Connected through hyperedge HE-4 (induced by source paper "pilot beam pattern design for channel estimation in massive mimo systems"):
  with members:
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "effects of channel aging in massive mimo systems"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "optimization of training and feedback overhead for beamforming over block fading channels"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-5 (induced by source paper "joint spatial division and multiplexing opportunistic beamforming and user grouping"):
  with members:
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "multi user diversity vs accurate channel feedback for mimo broadcast channels"

Connected through hyperedge HE-6 (induced by source paper "generic correlation increases noncoherent mimo capacity"):
  with members:
    - "a lower bound on the noncoherent capacity pre log for the mimo channel with temporally correlated fading"
    - "capacity pre log of noncoherent simo channels via hironaka s theorem"
    - "on the capacity of large mimo block fading channels"

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)