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: "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
  Abstract: Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the nonorthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multicell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator.

Connected through hyperedge HE-1 (induced by source paper "non uniform quantization codebook based hybrid precoding to reduce feedback overhead in millimeter wave 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"
    - "spectral and energy efficiency of multi pair massive mimo relay network with hybrid processing"
    - "pilot power allocation through user grouping in multi cell massive mimo systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "dynamic subarrays for hybrid precoding in wideband mmwave mimo systems"
    - "mixed adc massive mimo detectors performance analysis and design optimization"

Connected through hyperedge HE-2 (induced by source paper "cascaded channel estimation for large intelligent metasurface assisted massive mimo"):
  with members:
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "super resolution blind channel and signal estimation for massive mimo with arbitrary array geometry"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "bilinear generalized approximate message passing part i derivation"

Connected through hyperedge HE-3 (induced by source paper "dictionary learning for channel estimation in hybrid frequency selective mmwave mimo systems"):
  with members:
    - "spatial and frequency wideband effects in millimeter wave massive mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "separable dictionary learning"

Connected through hyperedge HE-4 (induced by source paper "overcoming the channel estimation barrier in massive mimo communication systems"):
  with members:
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "deep learning for massive mimo csi feedback"
    - "channel covariance estimation in massive mimo frequency division duplex systems"

Connected through hyperedge HE-5 (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"
    - "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"
    - "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-6 (induced by source paper "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation"):
  with members:
    - "denoising based turbo compressed sensing"
    - "approximate message passing with nearest neighbor sparsity pattern learning"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "fundamental limits of training based multiuser mimo systems"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"

Connected through hyperedge HE-7 (induced by source paper "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"):
  with members:
    - "time sequence channel inference for beam alignment in vehicular networks"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "dynamic channel acquisition in mu mimo"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "inferring remote channel state information cram er rao lower bound and deep learning implementation"):
  with members:
    - "a new view of multi user hybrid massive mimo non orthogonal angle division multiple access"
    - "how often should csi be updated for massive mimo systems with massive connectivity"
    - "remote channel inference for beamforming in ultra dense hyper cellular network"
    - "seeing the unobservable channel learning for wireless communication networks"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo 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)