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 for decoding of linear codes a syndrome based approach"
  Abstract: We present a novel framework for applying deep neural networks (DNN) to soft decoding of linear codes at arbitrary block lengths. Unlike other approaches, our framework allows unconstrained DNN design, enabling the free application of powerful designs that were developed in other contexts. Our method is robust to overfitting that inhibits many competing methods, which follows from the exponentially large number of codewords required for their training. We achieve this by transforming the channel output before feeding it to the network, extracting only the syndrome of the hard decisions and the channel output reliabilities. We prove analytically that this approach does not involve any intrinsic performance penalty, and guarantees the generalization of performance obtained during training. Our best results are obtained using a recurrent neural network (RNN) architecture combined with simple preprocessing by permutation. We provide simulation results that demonstrate performance that sometimes approaches that of the ordered statistics decoding (OSD) algorithm.

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 "reinforcement learning for channel coding learned bit flipping decoding"):
  with members:
    - "learning to flip successive cancellation decoding of polar codes with lstm networks"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "decoding reed muller codes using minimum weight parity checks"
    - "an introduction to deep learning for the physical layer"
    - "soft decision decoding of reed muller codes recursive lists"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"
    - "residual belief propagation informed scheduling for asynchronous message passing"

Connected through hyperedge HE-3 (induced by source paper "what can machine learning teach us about communications"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "wideband time domain digital backpropagation via subband processing and deep learning"
    - "asic implementation of time domain digital backpropagation with deep learned chromatic dispersion filters"
    - "nonlinear interference mitigation via deep neural networks"
    - "learning to decode linear codes using deep learning"
    - "multiple bases belief propagation decoding of high density cyclic codes"

Connected through hyperedge HE-4 (induced by source paper "active deep decoding of linear codes"):
  with members:
    - "learned belief propagation decoding with simple scaling and snr adaptation"
    - "what can machine learning teach us about communications"
    - "deep learning methods for improved decoding of linear codes"
    - "near maximum likelihood decoding with deep learning"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-5 (induced by source paper "deep learning assisted sum product detection algorithm for faster than nyquist signaling"):
  with members:
    - "cnn based signal detection for banded linear systems"
    - "deep learning methods for improved decoding of linear codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-6 (induced by source paper "learned belief propagation decoding with simple scaling and snr adaptation"):
  with members:
    - "what can machine learning teach us about communications"
    - "deep learning methods for improved decoding of linear codes"
    - "decoding reed muller codes using minimum weight parity checks"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-7 (induced by source paper "learning from the syndrome"):
  with members:
    - "online label recovery for deep learning based communication through error correcting codes"
    - "deep learning methods for improved decoding of linear codes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "rnn decoding of linear block codes"
    - "neural offset min sum decoding"
    - "learning to decode linear codes using deep learning"
    - "adam a method for stochastic optimization"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"

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)