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: "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
  Abstract: To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training and feedback overhead. In this paper, we consider multi-user massive MIMO systems and deploy the compressive sensing (CS) technique to reduce the training as well as the feedback overhead in the CSIT estimation. The multi-user massive MIMO systems exhibits a hidden joint sparsity structure in the user channel matrices due to the shared local scatterers in the physical propagation environment. As such, instead of naively applying the conventional CS to the CSIT estimation, we propose a distributed compressive CSIT estimation scheme so that the compressed measurements are observed at the users locally, while the CSIT recovery is performed at the base station jointly. A joint orthogonal matching pursuit recovery algorithm is proposed to perform the CSIT recovery, with the capability of exploiting the hidden joint sparsity in the user channel matrices. We analyze the obtained CSIT quality in terms of the normalized mean absolute error, and through the closed-form expressions, we obtain simple insights into how the joint channel sparsity can be exploited to improve the CSIT recovery performance.

Connected through hyperedge HE-1 (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-2 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "non stationarities in extra large scale massive mimo"
    - "multi band covariance interpolation with applications in massive mimo"
    - "yolov3 an incremental improvement"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"

Connected through hyperedge HE-3 (induced by source paper "deep learning assisted csi estimation for joint urllc and embb resource allocation"):
  with members:
    - "ultra reliable and low latency vehicular communication an active learning approach"
    - "embb urllc resource slicing a risk sensitive approach"
    - "wireless network intelligence at the edge"
    - "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "channel charting locating users within the radio environment using channel state information"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "machine learning in the air"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "federated learning over wireless fading channels"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "channel estimation and hybrid precoding for distributed phased arrays based mimo wireless communications"):
  with members:
    - "modeling and analyzing millimeter wave cellular systems"
    - "framework of channel estimation for hybrid analog and digital processing enabled massive mimo communications"
    - "5g cellular user equipment from theory to practical hardware design"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "codebook design for millimeter wave channel estimation with hybrid precoding structure"
    - "the world s first real time testbed for massive mimo design implementation and validation"
    - "an overview of transmission theory and techniques of large scale antenna systems for 5g wireless communications"

Connected through hyperedge HE-7 (induced by source paper "deep learning for csi feedback based on superimposed coding"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "learning to detect"
    - "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "deep learning in physical layer communications"
    - "mimo channel information feedback using deep recurrent network"
    - "multi task learning as multi objective optimization"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"

Connected through hyperedge HE-8 (induced by source paper "a covariance based hybrid channel feedback in fdd massive mimo systems"):
  with members:
    - "downlink precoding with mixed statistical and imperfect instantaneous csi for massive mimo systems"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"
    - "enabling covariance based feedback in massive mimo a user classification approach"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "pilot power allocation through user grouping in multi cell massive mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "antenna grouping based feedback compression for fdd based 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)