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 in physical layer communications"
  Abstract: Deep learning (DL) has shown the great potentials to break the bottleneck of communication systems. This article provides an overview on the recent advancements in DL-based physical layer communications. DL can improve the performance of each individual block in communication systems or optimize the whole transmitter/receiver. Therefore, we categorize the applications of DL in physical layer communications into systems with and without block structures. For DL-based communication systems with block structures, we demonstrate the power of DL in signal compression and signal detection. We also discuss the recent endeavors in developing end-to-end communication systems. Finally, the potential research directions are identified to boost the intelligent physical layer communications with DL.

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 coordinated beamforming for highly mobile millimeter wave systems"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-2 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo"):
  with members:
    - "adversarial attack on dl based massive mimo csi feedback"
    - "20 years of evolution from cognitive to intelligent communications"
    - "cnn based analog csi feedback in fdd mimo ofdm systems"
    - "deep learning based limited feedback designs for mimo systems"
    - "bit level optimized neural network for multi antenna channel quantization"
    - "an efficient deep learning framework for low rate massive mimo csi reporting"
    - "compression and acceleration of neural networks for communications"
    - "overcoming the channel estimation barrier in massive mimo communication systems"

Connected through hyperedge HE-3 (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 coordinated beamforming for highly mobile millimeter wave systems"
    - "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-4 (induced by source paper "data driven deep learning to design pilot and channel estimator for massive mimo"):
  with members:
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "over sampling codebook based hybrid minimum sum mean square error precoding for millimeter wave 3d mimo"
    - "compressive sensing techniques for next generation wireless communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"

Connected through hyperedge HE-5 (induced by source paper "online maneuver design for uav enabled noma systems via reinforcement learning"):
  with members:
    - "fundamental rate limits of uav enabled multiple access channel with trajectory optimization"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "path design for cellular connected uav with reinforcement learning"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "placement optimization of uav mounted mobile base stations"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"

Connected through hyperedge HE-6 (induced by source paper "adversarial attack on dl based massive mimo csi feedback"):
  with members:
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "adversarial images for variational autoencoders"
    - "convolutional radio modulation recognition networks"
    - "adversarial manipulation of deep representations"

Connected through hyperedge HE-7 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-8 (induced by source paper "deep transfer learning based downlink channel prediction for fdd massive mimo systems"):
  with members:
    - "fdd massive mimo based on efficient downlink channel reconstruction"
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "decaying momentum helps neural network training"
    - "learning to transfer unsupervised meta domain translation"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "model aided deep neural network for source number detection"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo 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)