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 "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.

Includes members:
  Member "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")
  Member "antenna grouping based feedback compression for fdd based massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a covariance based hybrid channel feedback in fdd massive mimo systems")
  Member "structured matching pursuit for reconstruction of dynamic sparse channels":
    also connected through other hyperedges:
      (none)
  Member "pilot beam pattern design for channel estimation in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure massive mimo communication with low resolution dacs")
  Member "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "dynamic compressive sensing of time varying signals via approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an approximate message passing framework for side information")

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)