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: "deep learning coordinated beamforming for highly mobile millimeter wave systems"
  Abstract: Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. First, the use of narrow beams and the sensitivity of mmWave signals to blockage greatly impact the coverage and reliability of highly-mobile links. Second, highly-mobile users in dense mmWave deployments need to frequently hand-off between base stations (BSs), which is associated with critical control and latency overhead. Further, identifying the optimal beamforming vectors in large antenna array mmWave systems requires considerable training overhead, which significantly affects the efficiency of these mobile systems. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. In the proposed solution, a number of distributed yet coordinating BSs simultaneously serve a mobile user. This user ideally needs to transmit only one uplink training pilot sequence that will be jointly received at the coordinating BSs using omni or quasi-omni beam patterns. These received signals draw a defining signature not only for the user location, but also for its interaction with the surrounding environment. The developed solution then leverages a deep learning model that learns how to use these signatures to predict the beamforming vectors at the BSs. This renders a comprehensive solution that supports highly-mobile mmWave applications with reliable coverage, low latency, and negligible training overhead. Simulation results show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead.

Connected through hyperedge HE-1 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming"):
  with members:
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "cnn based signal detection for banded linear systems"
    - "deep learning for radio resource allocation in multi cell networks"
    - "deep learning in physical layer communications"

Connected through hyperedge HE-2 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "learning to detect"
    - "artificial intelligence enabled cellular networks a critical path to beyond 5g and 6g"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "model driven deep learning for physical layer communications"

Connected through hyperedge HE-3 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks"):
  with members:
    - "adaptive millimeter wave communications exploiting mobility and blockage dynamics"
    - "second best beam alignment via bayesian multi armed bandits"
    - "active learning and csi acquisition for mmwave initial alignment"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "online learning for position aided millimeter wave beam training"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "a framework for automated cellular network tuning with reinforcement learning"
    - "deep learning in downlink coordinated multipoint in new radio heterogeneous networks"
    - "q learning algorithm for volte closed loop power control in indoor small cells"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning for launching and mitigating wireless jamming attacks"

Connected through hyperedge HE-5 (induced by source paper "beamforming design for large scale antenna arrays using deep learning"):
  with members:
    - "hybrid beamforming for millimeter wave systems using the mmse criterion"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-6 (induced by source paper "constructing deep neural networks with a priori knowledge of wireless tasks"):
  with members:
    - "optimizing wireless systems using unsupervised and reinforced unsupervised deep learning"
    - "learning to detect"
    - "deep learning for ul dl channel calibration in generic massive mimo systems"
    - "learning to optimize with unsupervised learning training deep neural networks for urllc"
    - "structure of deep neural networks with a priori information in wireless tasks"
    - "an lstm network for highway trajectory prediction"
    - "model driven deep learning for physical layer communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning for intelligent reflecting surfaces towards standalone operation"):
  with members:
    - "reconfigurable intelligent surface empowered 6g wireless communications challenges and opportunities"
    - "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination"
    - "holographic mimo surfaces for 6g wireless networks opportunities challenges and trends"
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"

Connected through hyperedge HE-8 (induced by source paper "intelligent uav base station selection in urban environments a supervised learning approach"):
  with members:
    - "performance analysis of mobile cellular connected drones under practical antenna configurations"
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "mobility in the sky performance and mobility analysis for cellular connected uavs"
    - "understanding uav cellular communications from existing networks to massive mimo"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "backhaul for low altitude uavs in urban environments"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

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)