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 to decode linear codes using deep learning"
  Abstract: A novel deep learning method for improving the belief propagation algorithm is proposed. The method generalizes the standard belief propagation algorithm by assigning weights to the edges of the Tanner graph. These edges are then trained using deep learning techniques. A well-known property of the belief propagation algorithm is the independence of the performance on the transmitted codeword. A crucial property of our new method is that our decoder preserved this property. Furthermore, this property allows us to learn only a single codeword instead of exponential number of code-words. Improvements over the belief propagation algorithm are demonstrated for various high density parity check codes.

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 "perm2vec graph permutation selection for decoding of error correction codes using self attention"):
  with members:
    - "from learning to meta learning reduced training overhead and complexity for communication systems"
    - "data driven ensembles for deep and hard decision hybrid decoding"
    - "active deep decoding of linear codes"
    - "data efficient mutual information neural estimator"
    - "a new permutation decoding method for reed muller codes"
    - "unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-3 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent"):
  with members:
    - "chebyshev inertial landweber algorithm for linear inverse problems"
    - "degrees of freedom analysis of unrolled neural networks"
    - "trainable ista for sparse signal recovery"
    - "deep learning aided trainable projected gradient decoding for ldpc codes"
    - "neural proximal gradient descent for compressive imaging"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "a model driven deep learning network for mimo detection"

Connected through hyperedge HE-4 (induced by source paper "neural enhanced belief propagation on factor graphs"):
  with members:
    - "factor graph neural network"
    - "iterative amortized inference"
    - "inference in probabilistic graphical models by graph neural networks"
    - "self normalizing neural networks"
    - "the consciousness prior"
    - "neural message passing for quantum chemistry"
    - "recurrent inference machines for solving inverse problems"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-5 (induced by source paper "data driven ensembles for deep and hard decision hybrid decoding"):
  with members:
    - "active deep decoding of linear codes"
    - "autoencoder based error correction coding for one bit quantization"
    - "deep learning methods for improved decoding of linear codes"
    - "a very brief introduction to machine learning with applications to communication systems"
    - "mine mutual information neural estimation"
    - "belief propagation list decoding of polar codes"
    - "near maximum likelihood decoding with deep learning"

Connected through hyperedge HE-6 (induced by source paper "pruning neural belief propagation decoders"):
  with members:
    - "learned belief propagation decoding with simple scaling and snr adaptation"
    - "deep learning methods for improved decoding of linear codes"
    - "decoding reed muller codes using minimum weight parity checks"
    - "adam a method for stochastic optimization"
    - "multiple bases belief propagation decoding of high density cyclic codes"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"

Connected through hyperedge HE-7 (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-8 (induced by source paper "machine learning for wireless communications in the internet of things a comprehensive survey"):
  with members:
    - "helper heterogeneous efficient low power radio for enabling ad hoc emergency public safety networks"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "over the air deep learning based radio signal classification"
    - "a non parametric multi stage learning framework for cognitive spectrum access in iot networks"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "fog computing survey of trends architectures requirements and research directions"

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)