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: "learning the mmse channel estimator"
  Abstract: We present a method for estimating conditionally Gaussian random vectors with random covariance matrices, which uses techniques from the field of machine learning. Such models are typical in communication systems, where the covariance matrix of the channel vector depends on random parameters, e.g., angles of propagation paths. If the covariance matrices exhibit certain Toeplitz and shift-invariance structures, the complexity of the minimum mean squared error (MMSE) channel estimator can be reduced to   ${\mathcal O}(M\log M)$    floating point operations, where   $M$    is the channel dimension. While in the absence of structure the complexity is much higher, we obtain a similarly efficient (but suboptimal) estimator by using the MMSE estimator of the structured model as a blueprint for the architecture of a neural network. This network learns the MMSE estimator for the unstructured model, but only within the given class of estimators that contains the MMSE estimator for the structured model. Numerical simulations with typical spatial channel models demonstrate the generalization properties of the chosen class of estimators to realistic channel models.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a regression approach to certain information transmission problems"):
  with members:
    - "learning to detect"
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "one bit ofdm receivers via deep learning"
    - "deepcode feedback codes via deep learning"
    - "a model driven deep learning network for mimo detection"
    - "a two step learning and interpolation method for location based channel database"
    - "mixed adc massive mimo"

Connected through hyperedge HE-3 (induced by source paper "channel estimation in massive mimo under hardware non linearities bayesian methods versus deep learning"):
  with members:
    - "max min rate of cell free massive mimo uplink with optimal uniform quantization"
    - "hardware distortion correlation has negligible impact on ul massive mimo spectral efficiency"
    - "massive mu mimo ofdm uplink with hardware impairments modeling and analysis"
    - "an introduction to deep learning for the physical layer"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "impact of spatial filtering on distortion from low noise amplifiers in massive mimo base stations"
    - "rate splitting to mitigate residual transceiver hardware impairments in massive mimo systems"
    - "analysis of massive mimo with hardware impairments and different channel models"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence enabled cellular networks a critical path to beyond 5g and 6g"):
  with members:
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "an introduction to deep learning for the physical layer"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-5 (induced by source paper "reproducible evaluation of neural network based channel estimators and predictors using a generic dataset"):
  with members:
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "learning the mmse channel predictor"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "learning the mmse channel predictor"):
  with members:
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a very brief introduction to machine learning with applications to communication systems"):
  with members:
    - "proactive received power prediction using machine learning and depth images for mmwave networks"
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "machine learning at the edge a data driven architecture with applications to 5g cellular networks"
    - "online label recovery for deep learning based communication through error correcting codes"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning in physical layer communications"
    - "deep learning methods for improved decoding of linear codes"

Connected through hyperedge HE-8 (induced by source paper "machine learning for link quality estimation a survey"):
  with members:
    - "machine learning based link fault identification and localization in complex networks"
    - "over the air deep learning based radio signal classification"
    - "deep learning methods for improved decoding of linear codes"
    - "electrosense open and big spectrum data"
    - "deep learning in mobile and wireless networking a survey"
    - "effective capacity in wireless networks a comprehensive survey"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"

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)