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: "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
  Abstract: We propose adjustable phase shift pilots (APSPs) for channel acquisition in wideband massive multiple-input multiple-output (MIMO) systems employing orthogonal frequency division multiplexing (OFDM) to reduce the pilot overhead. Based on a physically motivated channel model, we first establish a relationship between channel space-frequency correlations and the channel power angle-delay spectrum in the massive antenna array regime, which reveals the channel sparsity in massive MIMO-OFDM. With this channel model, we then investigate channel acquisition, including channel estimation and channel prediction, for massive MIMO-OFDM with APSPs. We show that channel acquisition performance in terms of sum mean square error can be minimized if the user terminals' channel power distributions in the angle-delay domain can be made nonoverlapping with proper pilot phase shift scheduling. A simplified pilot phase shift scheduling algorithm is developed based on this optimal channel acquisition condition. The performance of APSPs is investigated for both one symbol and multiple symbol data models. Simulations demonstrate that the proposed APSP approach can provide substantial performance gains in terms of achievable spectral efficiency over the conventional phase shift orthogonal pilot approach in typical mobility scenarios.

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"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep learning assisted csi estimation for joint urllc and embb resource allocation"):
  with members:
    - "ultra reliable and low latency vehicular communication an active learning approach"
    - "embb urllc resource slicing a risk sensitive approach"
    - "wireless network intelligence at the edge"
    - "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "channel charting locating users within the radio environment using channel state information"
    - "adam a method for stochastic optimization"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

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"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "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 "an efficient beam and channel acquisition via sparsity map and joint angle delay power profile estimation for wideband massive mimo systems"):
  with members:
    - "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "enhancing the estimation of mm wave large array channels by exploiting spatio temporal correlation and sparse scattering"
    - "a generalized framework on beamformer design and csi acquisition for single carrier massive mimo systems in millimeter wave channels"
    - "hybrid beamforming for massive mimo a survey"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "time varying massive mimo channel estimation capturing reconstruction and restoration"):
  with members:
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "interference alignment and soft space reuse based cooperative transmission for multi cell massive mimo networks"
    - "design and implementation of a tdd based 128 antenna massive mimo prototyping system"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"

Connected through hyperedge HE-6 (induced by source paper "performance of high mobility mimo communications with doppler diversity"):
  with members:
    - "bdma for millimeter wave terahertz massive mimo transmission with per beam synchronization"
    - "an overview of transmission theory and techniques of large scale antenna systems for 5g wireless communications"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-7 (induced by source paper "hierarchical restricted isometry property for kronecker product measurements"):
  with members:
    - "low overhead hierarchically sparse channel estimation for multiuser wideband massive mimo"
    - "secure massive iot using hierarchical fast blind deconvolution"
    - "performance of hierarchical sparse detectors for massive mtc"
    - "hierarchical sparse channel estimation for massive mimo"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "compressive random access using a common overloaded control channel"
    - "sparse signal processing concepts for efficient 5g system design"

Connected through hyperedge HE-8 (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"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "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"

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)