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: "mimo channel information feedback using deep recurrent network"
  Abstract: In a multiple-input multiple-output (MIMO) system, the availability of channel state information (CSI) at the transmitter is essential for performance improvement. Recent convolutional neural network (NN) based techniques show competitive ability in realizing CSI compression and feedback. By introducing a new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO communications. The proposed NN architecture invokes a module named long short-term memory (LSTM) which admits the NN to benefit from exploiting temporal and frequency correlations of wireless channels. Compromising performance with complexity, we further modify the NN architecture with a significantly reduced number of parameters to be trained. Finally, experiments show that the proposed NN architectures achieve better performance in terms of both CSI compression and recovery accuracy.

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 for massive mimo channel state acquisition and feedback"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "cnn based analog csi feedback in fdd mimo ofdm systems"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "multi resolution csi feedback with deep learning in massive mimo system"
    - "deep convolutional compression for massive mimo csi feedback"
    - "machine learning in the air"

Connected through hyperedge HE-3 (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-4 (induced by source paper "secure massive mimo communication with low resolution dacs"):
  with members:
    - "optimal multiuser loading in quantized massive mimo under spatially correlated channels"
    - "quantized constant envelope precoding with psk and qam signaling"
    - "on performance of quantized transceiver in multiuser massive mimo downlinks"
    - "spectral and energy efficiency of multi pair massive mimo relay network with hybrid processing"
    - "power efficient and secure wpcns with hardware impairments and non linear eh circuit"
    - "a tractable framework for performance analysis of dense multi antenna networks"
    - "analysis of one bit quantized precoding for the multiuser massive mimo downlink"
    - "analysis and design of secure massive mimo systems in the presence of hardware impairments"

Connected through hyperedge HE-5 (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"
    - "multi task learning as multi objective optimization"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "deep learning based csi feedback approach for time varying massive mimo channels"

Connected through hyperedge HE-6 (induced by source paper "resource allocation using gradient boosting aided deep q network for iot in c rans"):
  with members:
    - "bit level optimized neural network for multi antenna channel quantization"
    - "learning to optimize training deep neural networks for interference management"
    - "an introduction to deep reinforcement learning"
    - "deep forest"
    - "model driven deep learning for physical layer communications"
    - "deep forest towards an alternative to deep neural networks"
    - "xgboost a scalable tree boosting system"
    - "user association in 5g networks a survey and an outlook"

Connected through hyperedge HE-7 (induced by source paper "an efficient deep learning framework for low rate massive mimo csi reporting"):
  with members:
    - "bit level optimized neural network for multi antenna channel quantization"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "rethinking floating point for deep learning"
    - "training deep neural networks with 8 bit floating point numbers"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "deep learning for massive mimo csi feedback"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-8 (induced by source paper "deep learning based quantization of l values for gray coded modulation"):
  with members:
    - "deep log likelihood ratio quantization"
    - "deepcode feedback codes via deep learning"
    - "a survey on methods and theories of quantized neural networks"
    - "an introduction to deep learning for the physical layer"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "neural discrete representation learning"
    - "adam a method for stochastic optimization"
    - "quantized iterative message passing decoders with low error floor for ldpc codes"

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)