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: "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
  Abstract: The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. To reduce the overhead of the downlink training phase, we propose practical open-loop and closed-loop training frameworks in this paper. We assume the base station and the user share a common set of training signals in advance. In open-loop training, the base station transmits training signals in a round-robin manner, and the user successively estimates the current channel using long-term channel statistics such as temporal and spatial correlations and previous channel estimates. In closed-loop training, the user feeds back the best training signal to be sent in the future based on channel prediction and the previously received training signals. With a small amount of feedback from the user to the base station, closed-loop training offers better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.

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 "periodic analog channel estimation aided beamforming for massive mimo systems"):
  with members:
    - "hybrid beamforming with selection for multiuser massive mimo systems"
    - "fast analog beam tracking in phased antenna arrays theory and performance"
    - "omnidirectional precoding and combining based synchronization for millimeter wave massive mimo systems"
    - "design and analysis of initial access in millimeter wave cellular networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "initial access in 5g mmwave cellular networks"
    - "timing and carrier synchronization in wireless communication systems a survey and classification of research in the last 5 years"

Connected through hyperedge HE-3 (induced by source paper "continuous analog channel estimation aided beamforming for massive mimo systems"):
  with members:
    - "periodic analog channel estimation aided beamforming for massive mimo systems"
    - "hybrid beamforming with selection for multiuser massive mimo systems"
    - "massive mimo channel estimation for millimeter wave systems via matrix completion"
    - "position aided beam alignment for millimeter wave backhaul systems with large phased arrays"
    - "omnidirectional precoding and combining based synchronization for millimeter wave massive mimo systems"
    - "design and analysis of initial access in millimeter wave cellular networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "initial access in 5g mmwave cellular networks"

Connected through hyperedge HE-4 (induced by source paper "deep cnn based channel estimation for mmwave massive mimo systems"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"

Connected through hyperedge HE-5 (induced by source paper "time varying downlink channel tracking for quantized massive mimo networks"):
  with members:
    - "beamspace aware adaptive channel estimation for single carrier time varying massive mimo channels"
    - "interference alignment and soft space reuse based cooperative transmission for multi cell massive mimo networks"
    - "bayesian optimal data detector for mmwave ofdm system with low resolution adc"
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"

Connected through hyperedge HE-6 (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"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "channel estimation for spatially temporally correlated massive mimo systems with one bit adcs"):
  with members:
    - "channel estimation for one bit massive mimo systems exploiting spatio temporal correlations"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "channel estimation techniques for quantized distributed reception in mimo systems"

Connected through hyperedge HE-8 (induced by source paper "downlink pilot precoding and compressed channel feedback for fdd based cell free systems"):
  with members:
    - "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"
    - "on the total energy efficiency of cell free massive mimo"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "cell free massive mimo versus small cells"
    - "power allocation schemes for multicell massive mimo systems"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "pilot contamination and precoding in multi cell tdd systems"
    - "mimo broadcast channels with finite rate feedback"

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)