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 "interleaving channel estimation and limited feedback for point to point systems with a large number of transmit antennas"):
  Abstract: We introduce and investigate the opportunities of multi-antenna communication schemes whose training and feedback stages are interleaved and mutually interacting. Specifically, unlike the traditional schemes where the transmitter first trains all of its antennas at once and then receives a single feedback message, we consider a scenario where the transmitter instead trains its antennas one by one and receives feedback information immediately after training each one of its antennas. The feedback message may ask the transmitter to train another antenna; or, it may terminate the feedback/training phase and provide the quantized codeword (e.g., a beamforming vector) to be utilized for data transmission. As a specific application, we consider a multiple-input single-output system with $t$ transmit antennas, a short-term power constraint $P$, and target data rate $\rho$. We show that for any $t$, the same outage probability as a system with perfect transmitter and receiver channel state information can be achieved with a feedback rate of $R_1$ bits per channel state and via training $R_2$ transmit antennas on average, where $R_1$ and $R_2$ are independent of $t$, and depend only on $\rho$ and $P$. In addition, we design variable-rate quantizers for channel coefficients to further minimize the feedback rate of our scheme.

Includes members:
  Member "interleaved training and training based transmission design for hybrid massive antenna downlink":
    also connected through other hyperedges:
      (none)
  Member "compressed channel feedback for correlated massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high dimensional stochastic gradient quantization for communication efficient edge learning")
  Member "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "a covariance based hybrid channel feedback in fdd massive mimo systems")
  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 "training sequence design for feedback assisted hybrid beamforming in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "time varying downlink channel tracking for quantized massive mimo networks")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint power control and lsfd for wireless powered cell free 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-7 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")

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)