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: "achievable rates of fdd massive mimo systems with spatial channel correlation"
  Abstract: It is well known that the performance of frequency-division-duplex (FDD) massive MIMO systems with i.i.d. channels is disappointing compared with that of time-division-duplex (TDD) systems, due to the prohibitively large overhead for acquiring channel state information at the transmitter (CSIT). In this paper, we investigate the achievable rates of FDD massive MIMO systems with spatially correlated channels, considering the CSIT acquisition dimensionality loss, the imperfection of CSIT and the regularized-zero-forcing linear precoder. The achievable rates are optimized by judiciously designing the downlink channel training sequences and user CSIT feedback codebooks, exploiting the multiuser spatial channel correlation. We compare our achievable rates with TDD massive MIMO systems, i.i.d. FDD systems, and the joint spatial division and multiplexing (JSDM) scheme, by deriving the deterministic equivalents of the achievable rates, based on popular channel models. It is shown that, based on the proposed eigenspace channel estimation schemes, the rate-gap between FDD systems and TDD systems is significantly narrowed, even approached under moderate number of base station antennas. Compared to the JSDM scheme, our proposal achieves dimensionality-reduction channel estimation without channel pre-projection, and higher throughput for moderate number of antennas and moderate to large channel coherence time, though at higher computational complexity.

Connected through hyperedge HE-1 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "cnn based analog csi feedback in fdd mimo ofdm systems"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "multi resolution csi feedback with deep learning in massive mimo system"
    - "deep convolutional compression for massive mimo csi feedback"
    - "machine learning in the air"

Connected through hyperedge HE-2 (induced by source paper "covariance aided csi acquisition with non orthogonal pilots in massive mimo a large system performance analysis"):
  with members:
    - "massive mimo has unlimited capacity"
    - "pilot length optimization for spatially correlated multi user mimo channel estimation"
    - "snops short non orthogonal pilot sequences for downlink channel state estimation in fdd massive mimo"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "dealing with interference in distributed large scale mimo systems a statistical approach"
    - "massive mimo for next generation wireless systems"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "covariance aided csi acquisition with non orthogonal pilots in massive mimo systems"):
  with members:
    - "massive mimo has unlimited capacity"
    - "pilot length optimization for spatially correlated multi user mimo channel estimation"
    - "dealing with interference in distributed large scale mimo systems a statistical approach"
    - "massive mimo for next generation wireless systems"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-4 (induced by source paper "enabling fdd massive mimo through deep learning based channel prediction"):
  with members:
    - "massive mimo has unlimited capacity"
    - "compressive sensing techniques for next generation wireless communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"
    - "massive mimo ten myths and one critical question"
    - "very deep convolutional networks for large scale image recognition"
    - "massive mimo for next generation wireless systems"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-5 (induced by source paper "ergodic spectral efficiency of massive mimo with correlated rician channel and mrc detection based on ls and mmse channel estimation"):
  with members:
    - "the effect of diversity combining on isi in massive mimo"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "what will 5g be"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-6 (induced by source paper "hybrid precoding architecture for massive multiuser mimo with dissipation sub connected or fully connected structures"):
  with members:
    - "on performance of quantized transceiver in multiuser massive mimo downlinks"
    - "spectral and energy efficiency of multi pair massive mimo relay network with hybrid processing"
    - "multiuser millimeter wave beamforming strategies with quantized and statistical csit"
    - "two dimensional aod and aoa acquisition for wideband millimeter wave systems with dual polarized mimo"
    - "hybrid analog digital precoding revisited under realistic rf modeling"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "linear precoding of data and artificial noise in secure massive mimo systems"

Connected through hyperedge HE-7 (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"
    - "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"
    - "the world s first real time testbed for massive mimo design implementation and validation"

Connected through hyperedge HE-8 (induced by source paper "optimal discrete spatial compression for beamspace massive mimo signals"):
  with members:
    - "a real time millimeter wave phased array mimo channel sounder"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "spatio temporal waveform design for multiuser massive mimo downlink with 1 bit receivers"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "cost effective millimeter wave communications with lens antenna array"
    - "fronthaul compression and transmit beamforming optimization for multi antenna uplink c ran"
    - "quantized precoding for massive mu mimo"
    - "hybrid beamforming for massive mimo a survey"

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)