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: "a deep learning framework for optimization of miso downlink beamforming"
  Abstract: Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for realtime implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

Connected through hyperedge HE-1 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration"):
  with members:
    - "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"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "model driven deep learning for physical layer communications"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination"):
  with members:
    - "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 learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning for launching and mitigating wireless jamming attacks"

Connected through hyperedge HE-3 (induced by source paper "model driven beamforming neural networks"):
  with members:
    - "a model driven deep learning network for mimo detection"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"

Connected through hyperedge HE-4 (induced by source paper "neural network optimized 1 bit precoding for massive mu mimo"):
  with members:
    - "multilevel mimo detection with deep learning"
    - "one bit massive mimo precoding via a minimum symbol error probability design"
    - "quantized constant envelope precoding with psk and qam signaling"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "a model driven deep learning network for mimo detection"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "1 bit massive mu mimo precoding in vlsi"
    - "quantized precoding for multi antenna downlink channels with magiq"

Connected through hyperedge HE-5 (induced by source paper "sum rate maximization of multicell miso networks with limited information exchange"):
  with members:
    - "opportunistic downlink interference alignment for multi cell mimo networks"
    - "coordinated regularized zero forcing precoding for multicell miso systems with limited feedback"
    - "joint transmission with limited backhaul connectivity"
    - "analysis of massive mimo enabled downlink wireless backhauling for full duplex small cells"
    - "sinr and throughput scaling in ultradense urban cellular networks"
    - "on the spatial degrees of freedom of multicell and multiuser mimo channels"
    - "on feasibility of interference alignment in mimo interference networks"
    - "downlink interference alignment"

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)