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 "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"):
  Abstract: We propose a novel method for massive Multiple-Input Multiple-Output (massive MIMO) in Frequency Division Duplexing (FDD) systems. Due to the large frequency separation between Uplink (UL) and Downlink (DL), in FDD systems channel reciprocity does not hold. Hence, in order to provide DL channel state information to the Base Station (BS), closed-loop DL channel probing and feedback is needed. In massive MIMO this incurs typically a large training overhead. For example, in a typical configuration with $M \simeq 200$ BS antennas and fading coherence block of $T \simeq 200$ symbols, the resulting rate penalty factor due to the DL training overhead, given by $\max\{0, 1 - M/T\}$, is close to 0. To reduce this overhead, we build upon the observation that the Angular Scattering Function (ASF) of the user channels is invariant over the frequency domain. We develop a robust and stable method to estimate the users' DL channel covariance matrices from pilots sent by the users in the UL. The resulting DL covariance information is used to optimize a sparsifying precoder, in order to limit the effective channel dimension of each user channel to be not larger than some desired DL pilot dimension $T_{\rm dl}$. In this way, we can maximize the rank of the effective sparsified channel matrix subject to a desired training overhead penalty factor $\max\{0, 1 - T_{\rm dl} / T\}$. We pose this problem as a Mixed Integer Linear Program, that can be efficiently solved. Furthermore, each user can simply feed back its $T_{\rm dl}$ pilot measurements. Thus, the proposed approach yields also a small feedback overhead and delay. We provide simulation results demonstrating the superiority of the proposed approach with respect to state-of-the-art "compressed DL pilot" schemes based on compressed sensing.

Includes members:
  Member "multi band covariance interpolation with applications in 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 "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on optimal scheduling for joint spatial division and multiplexing approach in fdd massive mimo")
  Member "channel estimation for tdd fdd massive mimo systems with channel covariance computing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")
  Member "millimeter wave beam selection using out of band spatial information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for mmwave beam and blockage prediction using sub 6ghz channels")
  Member "on the ergodic rate lower bounds with applications to massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of uav communications in cell free massive mimo systems")
  Member "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "statistical beamforming for fdd downlink massive mimo via spatial information extraction and beam selection")
  Member "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "security vulnerability of fdd massive mimo systems in downlink training phase")
  Member "the world s first real time testbed for massive mimo design implementation and validation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the pilot contamination attack in multi cell multiuser massive mimo networks")

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)