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 channel training and feedback for fdd massive mimo systems"
  Abstract: Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance gains in massive MIMO systems, accurate channel-state information at the transmitter (CSIT) is crucial. Due to the overwhelming pilot signaling and channel feedback overhead, however, conventional downlink channel estimation and uplink channel feedback schemes might not be suitable for frequency-division duplexing (FDD) massive MIMO systems. In addition, these two topics are usually separately considered in the literature. In this paper, we propose a joint channel training and feedback scheme for FDD massive MIMO systems. Specifically, we first exploit the temporal correlation of time-varying channels to propose a differential channel training and feedback scheme, which simultaneously reduces the overhead for downlink training and uplink feedback. We next propose a structured compressive sampling matching pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the structured sparsity of wireless MIMO channels. Simulation results demonstrate that the proposed scheme can achieve substantial reduction in the training and feedback overhead.

Connected through hyperedge HE-1 (induced by source paper "downlink pilot precoding and compressed channel feedback for fdd based cell free systems"):
  with members:
    - "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"
    - "on the total energy efficiency of cell free massive mimo"
    - "cell free massive mimo versus small cells"
    - "power allocation schemes for multicell massive mimo systems"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "pilot contamination and precoding in multi cell tdd systems"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-2 (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"
    - "compressive sensing based multiuser detector for the large scale sm mimo uplink"
    - "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-3 (induced by source paper "security vulnerability of fdd massive mimo systems in downlink training phase"):
  with members:
    - "a survey of physical layer security techniques for 5g wireless networks and challenges ahead"
    - "jamming resistant receivers for the massive mimo uplink"
    - "jamming detection in massive mimo systems"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"
    - "subverting massive mimo by smart jamming"
    - "secure massive mimo transmission with an active eavesdropper"
    - "physical layer security for massive mimo an overview on passive eavesdropping and active attacks"

Connected through hyperedge HE-4 (induced by source paper "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"):
  with members:
    - "limited feedback channel estimation in massive mimo with non uniform directional dictionaries"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "compressed channel feedback for correlated massive mimo systems"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"

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)