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: "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.

Connected through hyperedge HE-1 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit"):
  with members:
    - "beam domain secure transmission for massive mimo communications"
    - "fractional programming for communication systems part i power control and beamforming"
    - "energy efficient multicell multigroup multicasting with joint beamforming and antenna selection"
    - "network deployment for maximal energy efficiency in uplink with multislope path loss"
    - "energy efficient beam coordination strategies with rate dependent processing power"
    - "bdma for millimeter wave terahertz massive mimo transmission with per beam synchronization"
    - "free deterministic equivalents for the analysis of mimo multiple access channel"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"

Connected through hyperedge HE-2 (induced by source paper "statistical beamforming for fdd downlink massive mimo via spatial information extraction and beam selection"):
  with members:
    - "semi blind channel and signal estimation for uplink massive mimo with channel sparsity"
    - "multi band covariance interpolation with applications in massive mimo"
    - "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "massive mimo for next generation wireless systems"
    - "statistical beamforming on the grassmann manifold for the two user broadcast channel"

Connected through hyperedge HE-3 (induced by source paper "massive mimo transmission for leo satellite communications"):
  with members:
    - "architectures and key technical challenges for 5g systems incorporating satellites"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "statistical eigenmode transmission over jointly correlated mimo channels"

Connected through hyperedge HE-4 (induced by source paper "a covariance based hybrid channel feedback in fdd massive mimo systems"):
  with members:
    - "downlink precoding with mixed statistical and imperfect instantaneous csi for massive mimo systems"
    - "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"
    - "enabling covariance based feedback in massive mimo a user classification approach"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "pilot power allocation through user grouping in multi cell massive mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "dealing with interference in distributed large scale mimo systems a statistical approach"

Connected through hyperedge HE-5 (induced by source paper "fdd massive mimo based on efficient downlink channel reconstruction"):
  with members:
    - "dft based hybrid beamforming multiuser systems rate analysis and beam selection"
    - "multi band covariance interpolation with applications in massive mimo"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-6 (induced by source paper "machine learning for geometrically consistent angular spread function estimation in massive mimo"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for joint source channel coding of text"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"

Connected through hyperedge HE-7 (induced by source paper "uplink downlink channel covariance transformations and precoding design for fdd massive mimo"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "5g new radio unveiling the essentials of the next generation wireless access technology"
    - "deep learning for joint source channel coding of text"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-8 (induced by source paper "passive radar at the roadside unit to configure millimeter wave vehicle to infrastructure links"):
  with members:
    - "an approximate message passing framework for side information"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "millimeter wave beam selection using out of band spatial information"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "hybrid beamforming for massive mimo a survey"

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)