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: "power of deep learning for channel estimation and signal detection in ofdm systems"
  Abstract: This article presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM). OFDM has been widely adopted in wireless broadband communications to combat frequency-selective fading in wireless channels. In this article, we take advantage of deep learning in handling wireless OFDM channels in an end-to-end approach. Different from existing OFDM receivers that first estimate CSI explicitly and then detect/recover the transmitted symbols with the estimated CSI, our deep learning based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from the simulation based on the channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach has the ability to address channel distortions and detect the transmitted symbols with performance comparable to minimum mean-square error (MMSE) estimator. Furthermore, the deep learning based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix (CP) is omitted, and nonlinear clipping noise is presented. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortions and interferences.

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"
    - "deep learning in physical layer communications"
    - "multi user precoding and channel estimation for hybrid millimeter wave 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 "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"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "model driven deep learning for physical layer communications"

Connected through hyperedge HE-5 (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"
    - "deep learning in physical layer communications"
    - "compressive sensing techniques for next generation wireless communications"
    - "deep learning for massive mimo csi feedback"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"

Connected through hyperedge HE-6 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "model free training of end to end communication systems"
    - "a generic complementary sequence encoder"
    - "an introduction to deep learning for the physical layer"
    - "suppressing alignment joint papr and out of band power leakage reduction for ofdm based systems"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"

Connected through hyperedge HE-7 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems"):
  with members:
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "deep learning in neural networks"
    - "energy based modulation for noncoherent massive simo systems"
    - "performance analysis of energy detection based massive simo"

Connected through hyperedge HE-8 (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 in physical layer communications"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep learning for massive mimo csi feedback"
    - "adversarial images for variational autoencoders"
    - "convolutional radio modulation recognition networks"
    - "adversarial manipulation of deep representations"

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)