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: "compressed sensing based multi user millimeter wave systems how many measurements are needed"
  Abstract: Millimeter wave (mmWave) systems will likely employ directional beamforming with large antenna arrays at both the transmitters and receivers. Acquiring channel knowledge to design these beamformers, however, is challenging due to the large antenna arrays and small signal-to-noise ratio before beamforming. In this paper, we propose and evaluate a downlink system operation for multi-user mmWave systems based on compressed sensing channel estimation and conjugate analog beamforming. Adopting the achievable sum-rate as a performance metric, we show how many compressed sensing measurements are needed to approach the perfect channel knowledge performance. The results illustrate that the proposed algorithm requires an order of magnitude less training overhead compared with traditional lower-frequency solutions, while employing mmWave-suitable hardware. They also show that the number of measurements need to be optimized to handle the trade-off between the channel estimate quality and the training overhead.

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 "channel estimation and hybrid beamforming for reconfigurable intelligent surfaces assisted thz communications"):
  with members:
    - "secure wireless communication via intelligent reflecting surface"
    - "capacity characterization for intelligent reflecting surface aided mimo communication"
    - "intelligent reflecting surface meets ofdm protocol design and rate maximization"
    - "intelligent reflecting surface design for mimo system by maximizing sum path gains"
    - "channel estimation and transmission for intelligent reflecting surface assisted thz communications"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"

Connected through hyperedge HE-3 (induced by source paper "beam squint and channel estimation for wideband mmwave massive mimo ofdm systems"):
  with members:
    - "spatial and frequency wideband effects in millimeter wave massive mimo systems"
    - "beamforming codebook compensation for beam squint with channel capacity constraint"
    - "a new view of multi user hybrid massive mimo non orthogonal angle division multiple access"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "effect of wideband beam squint on codebook design in phased array wireless systems"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"

Connected through hyperedge HE-4 (induced by source paper "fast accurate beam and channel tracking for two dimensional phased antenna array"):
  with members:
    - "how to mobilize mmwave a joint beam and channel tracking approach"
    - "analog beam tracking in linear antenna arrays convergence optimality and performance"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "auxiliary beam pair enabled aod and aoa estimation in closed loop large scale mmwave mimo systems"
    - "mobile millimeter wave channel acquisition tracking and abrupt change detection"
    - "28 ghz millimeter wave ultrawideband small scale fading models in wireless channels"
    - "enabling uav cellular with millimeter wave communication potentials and approaches"
    - "hybrid beamforming for massive mimo a survey"

Connected through hyperedge HE-5 (induced by source paper "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"):
  with members:
    - "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"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "generative adversarial estimation of channel covariance in vehicular millimeter wave systems"
    - "deep complex networks"
    - "an overview of multi task learning in deep neural networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-6 (induced by source paper "compressed csi feedback with learned measurement matrix for mmwave massive mimo"):
  with members:
    - "a fast noniterative algorithm for compressive sensing using binary measurement matrices"
    - "compressive sensing techniques for next generation wireless communications"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "compressed sensing for wireless communications useful tips and tricks"

Connected through hyperedge HE-7 (induced by source paper "deep learning coordinated beamforming for highly mobile millimeter wave systems"):
  with members:
    - "modeling and analyzing millimeter wave cellular systems"
    - "position aided beam alignment for millimeter wave backhaul systems with large phased arrays"
    - "base station diversity propagation measurements at 73 ghz millimeter wave for 5g coordinated multipoint comp analysis"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "intra cluster characteristics of 28 ghz wireless channel in urban micro street canyon"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"

Connected through hyperedge HE-8 (induced by source paper "efficient beam alignment for mmwave single carrier systems with hybrid mimo transceivers"):
  with members:
    - "single carrier modulation versus ofdm for millimeter wave wireless mimo"
    - "throughput optimal beam alignment in millimeter wave networks"
    - "frequency domain compressive channel estimation for frequency selective hybrid mmwave mimo systems"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "a robust time domain beam alignment scheme for multi user wideband mmwave systems"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "channel estimation for hybrid architecture based wideband millimeter wave 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)