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: "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"
  Abstract: We consider the problem of downlink training and channel estimation in frequency division duplex (FDD) massive MIMO systems, where the base station (BS) equipped with a large number of antennas serves a number of single-antenna users simultaneously. To obtain the channel state information (CSI) at the BS in FDD systems, the downlink channel has to be estimated by users via downlink training and then fed back to the BS. For FDD large-scale MIMO systems, the overhead for downlink training and CSI uplink feedback could be prohibitively high, which presents a significant challenge. In this paper, we study the behavior of the minimum mean-squared error (MMSE) estimator when the channel covariance matrix has a low-rank or an approximate low-rank structure. Our theoretical analysis reveals that the amount of training overhead can be substantially reduced by exploiting the low-rank property of the channel covariance matrix. In particular, we show that the MMSE estimator is able to achieve exact channel recovery in the asymptotic low-noise regime, provided that the number of pilot symbols in time is no less than the rank of the channel covariance matrix. We also present an optimal pilot design for the single-user case, and an asymptotic optimal pilot design for the multi-user scenario. Lastly, we develop a simple model-based scheme to estimate the channel covariance matrix, based on which the MMSE estimator can be employed to estimate the channel. The proposed scheme does not need any additional training overhead. Simulation results are provided to verify our theoretical results and illustrate the effectiveness of the proposed estimated covariance-assisted MMSE estimator.

Connected through hyperedge HE-1 (induced by source paper "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "millimeter wave beam selection using out of band spatial information"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "the world s first real time testbed for massive mimo design implementation and validation"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"

Connected through hyperedge HE-2 (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"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "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-3 (induced by source paper "linear shrinkage estimation of covariance matrices using low complexity cross validation"):
  with members:
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"

Connected through hyperedge HE-4 (induced by source paper "quantization design and channel estimation for massive mimo systems with one bit adcs"):
  with members:
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "mixed adc massive mimo"
    - "one bit massive mimo channel estimation and high order modulations"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"

Connected through hyperedge HE-5 (induced by source paper "channel estimation for tdd fdd massive mimo systems with channel covariance computing"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"

Connected through hyperedge HE-6 (induced by source paper "on the ergodic rate lower bounds with applications to massive mimo"):
  with members:
    - "channel hardening and favorable propagation in cell free massive mimo with stochastic geometry"
    - "on the role of transmit correlation diversity in multiuser mimo systems"
    - "no downlink pilots are needed in tdd massive mimo"
    - "cell free massive mimo uniformly great service for everyone"
    - "achieving large multiplexing gain in distributed antenna systems via cooperation with pcell technology"
    - "massive mimo for next generation wireless systems"
    - "massive mu mimo downlink tdd systems with linear precoding and downlink pilots"
    - "joint spatial division and multiplexing for mm wave channels"

Connected through hyperedge HE-7 (induced by source paper "location aided pilot decontamination for massive mimo systems"):
  with members:
    - "massive mimo has unlimited capacity"
    - "adaptive pilot clustering in heterogeneous massive mimo networks"
    - "mitigating pilot contamination through location aware pilot assignment in massive mimo networks"
    - "performance analysis of massive mimo for cell boundary users"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "smart pilot assignment for massive mimo"
    - "massive mimo for next generation wireless systems"

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)