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: "deep learning methods for improved decoding of linear codes"
  Abstract: The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.

Connected through hyperedge HE-1 (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-2 (induced by source paper "design of capacity approaching low density parity check codes using recurrent neural networks"):
  with members:
    - "ai coding learning to construct error correction codes"
    - "deep joint source channel coding for wireless image transmission"
    - "an introduction to deep learning for the physical layer"
    - "imposing hard constraints on deep networks promises and limitations"
    - "on deep learning based channel decoding"
    - "an overview of gradient descent optimization algorithms"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-4 (induced by source paper "admm based decoder for binary linear codes aided by deep learning"):
  with members:
    - "amp inspired deep networks for sparse linear inverse problems"
    - "the admm penalized decoder for ldpc codes"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "decomposition methods for large scale lp decoding"

Connected through hyperedge HE-5 (induced by source paper "pruning neural belief propagation decoders"):
  with members:
    - "learned belief propagation decoding with simple scaling and snr adaptation"
    - "decoding reed muller codes using minimum weight parity checks"
    - "learning to decode linear codes using deep learning"
    - "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-6 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission"):
  with members:
    - "learning modulation design for swipt with nonlinear energy harvester large and small signal power regimes"
    - "signal and system design for wireless power transfer prototype experiment and validation"
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "model free training of end to end communication systems"
    - "a learning approach to wireless information and power transfer signal and system design"
    - "an introduction to deep learning for the physical layer"
    - "on the capacity of swipt systems with a nonlinear energy harvesting circuit"

Connected through hyperedge HE-7 (induced by source paper "turbo autoencoder deep learning based channel codes for point to point communication channels"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "neural joint source channel coding"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "don t decay the learning rate increase the batch size"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-8 (induced by source paper "learning to detect"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "learning approximate neural estimators for wireless channel state information"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "rnn decoding of linear block codes"
    - "detection algorithms for communication systems using deep learning"
    - "deep learning based mimo communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "onsager corrected deep learning for sparse linear inverse problems"

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)