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: "joint csit acquisition based on low rank matrix completion for fdd massive mimo systems"
  Abstract: Channel state information at the transmitter (CSIT) is essential for frequency-division duplexing (FDD) massive MIMO systems, but conventional solutions involve overwhelming overhead both for downlink channel training and uplink channel feedback. In this letter, we propose a joint CSIT acquisition scheme to reduce the overhead. Particularly, unlike conventional schemes where each user individually estimates its own channel and then feed it back to the base station (BS), we propose that all scheduled users directly feed back the pilot observation to the BS, and then joint CSIT recovery can be realized at the BS. We further formulate the joint CSIT recovery problem as a low-rank matrix completion problem by utilizing the low-rank property of the massive MIMO channel matrix, which is caused by the correlation among users. Finally, we propose a hybrid low-rank matrix completion algorithm based on the singular value projection to solve this problem. Simulations demonstrate that the proposed scheme can provide accurate CSIT with lower overhead than conventional schemes.

Connected through hyperedge HE-1 (induced by source paper "low rank matrix completion a contemporary survey"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "overview of full dimension mimo in lte advanced pro"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "matrix completion based channel estimation for mmwave communication systems with array inherent impairments"):
  with members:
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "codebook design for millimeter wave channel estimation with hybrid precoding structure"
    - "channel estimation for mmwave massive mimo based access and backhaul in ultra dense network"
    - "hybrid analog digital channel estimation and beamforming training throughput tradeoff"

Connected through hyperedge HE-3 (induced by source paper "a graph theoretic approach for training overhead reduction in fdd massive mimo systems"):
  with members:
    - "a novel alternative to cloud ran for throughput densification coded pilots and fast user packet scheduling at remote radio heads"
    - "directional training and fast sector based processing schemes for mmwave channels"
    - "massive mimo ten myths and one critical question"
    - "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"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "joint spatial division and multiplexing for mm wave channels"

Connected through hyperedge HE-4 (induced by source paper "optimal weighted low rank matrix recovery with subspace prior information"):
  with members:
    - "weighted matrix completion and recovery with prior subspace information"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-5 (induced by source paper "multi user precoding and channel estimation for hybrid millimeter wave systems"):
  with members:
    - "modeling and analyzing millimeter wave cellular systems"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "doubly massive mmwave mimo systems using very large antenna arrays at both transmitter and receiver"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "achievable rate of rician large scale mimo channels with transceiver hardware impairments"
    - "massive mimo ten myths and one critical question"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"

Connected through hyperedge HE-6 (induced by source paper "channel training for analog fdd repeaters optimal estimators and cramer rao bounds"):
  with members:
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-7 (induced by source paper "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"):
  with members:
    - "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"
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"

Connected through hyperedge HE-8 (induced by source paper "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"):
  with members:
    - "asymptotic orthogonality analysis of time domain sparse massive mimo channels"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "priori information aided iterative hard threshold a low complexity high accuracy compressive sensing based channel estimation for tds ofdm"
    - "structured compressive sensing based superimposed pilot design in downlink large scale mimo systems"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "super resolution sparse mimo ofdm channel estimation based on spatial and temporal correlations"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"

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)