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: "rnn decoding of linear block codes"
  Abstract: Designing a practical, low complexity, close to optimal, channel decoder for powerful algebraic codes with short to moderate block length is an open research problem. Recently it has been shown that a feed-forward neural network architecture can improve on standard belief propagation decoding, despite the large example space. In this paper we introduce a recurrent neural network architecture for decoding linear block codes. Our method shows comparable bit error rate results compared to the feed-forward neural network with significantly less parameters. We also demonstrate improved performance over belief propagation on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the RNN decoder can be used to improve the performance or alternatively reduce the computational complexity of the mRRD algorithm for low complexity, close to optimal, decoding 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 "learning to detect"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning to authenticate with deep multibiometric hashing and neural network decoding"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "deep learning methods for improved decoding of linear codes"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "multibiometric secure system based on deep learning"
    - "neural offset min sum decoding"
    - "learning to decode linear codes using deep learning"
    - "the nd iris 0405 iris image dataset"

Connected through hyperedge HE-4 (induced by source paper "deep learning methods for improved decoding of linear codes"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "performance bounds of concatenated polar coding schemes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "detection algorithms for communication systems using deep learning"
    - "neural offset min sum decoding"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"
    - "code design for short blocks a survey"

Connected through hyperedge HE-5 (induced by source paper "belief propagation decoding of polar codes on permuted factor graphs"):
  with members:
    - "mitigating clipping effects on error floors under belief propagation decoding of polar codes"
    - "partitioned successive cancellation list decoding of polar codes"
    - "improving belief propagation decoding of polar codes using scattered exit charts"
    - "combining belief propagation and successive cancellation list decoding of polar codes on a gpu platform"
    - "flexible length polar codes through graph based augmentation"
    - "list decoding of polar codes"
    - "how to construct polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-6 (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"
    - "deep learning for decoding of linear codes a syndrome based approach"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "an introduction to deep learning for the physical layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "learning to protect communications with adversarial neural cryptography"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "detection algorithms for communication systems using deep learning"
    - "on deep learning based channel decoding"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "unsupervised representation learning of structured radio communication signals"
    - "reinforcement learning with unsupervised auxiliary tasks"

Connected through hyperedge HE-8 (induced by source paper "deep learning for wireless physical layer opportunities and challenges"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "learning the mmse channel estimator"
    - "an introduction to deep learning for the physical layer"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "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"
    - "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)