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: "belief propagation list decoding of polar codes"
  Abstract: We propose a belief propagation list (BPL) decoder with comparable performance to the successive cancellation list (SCL) decoder of polar codes, which already achieves the maximum likelihood (ML) bound of polar codes for sufficiently large list size    $L$   . The proposed decoder is composed of multiple parallel independent belief propagation (BP) decoders based on differently permuted polar code factor graphs. A list of possible transmitted codewords is generated and the one closest to the received vector, in terms of Euclidean distance, is picked. To the best of our knowledge, the proposed BPL decoder provides the best performance of plain polar codes under iterative decoding known so far. The proposed algorithm does not require any changes in the polar code structure itself, rendering the BPL into an alternative to the SCL decoder, equipped with a soft output capability enabling, e.g., iterative detection and decoding to further improve performance. Further benefits are the lower decoding latency than the SCL decoder and the possibility of high throughput implementations. Additionally, we show that a different selection strategy of frozen bit positions can further enhance the error-rate performance of the proposed decoder.

Connected through hyperedge HE-1 (induced by source paper "polar codes and their quantum domain counterparts"):
  with members:
    - "reinforcement learning for nested polar code construction"
    - "decoder tailored polar code design using the genetic algorithm"
    - "genetic algorithm based polar code construction for the awgn channel"
    - "improved bit flipping algorithm for successive cancellation decoding of polar codes"
    - "design of polar codes in 5g new radio"
    - "belief propagation decoding of polar codes on permuted factor graphs"
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "dynamic scflip decoding of polar codes"

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 "crc aided belief propagation list decoding of polar codes"):
  with members:
    - "partially permuted multi trellis belief propagation for polar codes"
    - "on the decoding of polar codes on permuted factor graphs"
    - "neural belief propagation decoding of crc polar concatenated codes"
    - "belief propagation decoding of polar codes on permuted factor graphs"
    - "decoding reed muller and polar codes by successive factor graph permutations"
    - "mitigating clipping effects on error floors under belief propagation decoding of polar codes"
    - "combining belief propagation and successive cancellation list decoding of polar codes on a gpu platform"
    - "flexible length polar codes through graph based augmentation"

Connected through hyperedge HE-4 (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"
    - "near maximum likelihood decoding with deep learning"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-5 (induced by source paper "scan list decoding of polar codes"):
  with members:
    - "construction and decoding of product codes with non systematic polar codes"
    - "permutation decoding of polar codes"
    - "on the decoding of polar codes on permuted factor graphs"
    - "belief propagation decoding of polar codes on permuted factor graphs"
    - "a comparative study of polar code constructions for the awgn channel"
    - "list decoding of polar codes"

Connected through hyperedge HE-6 (induced by source paper "decoder tailored polar code design using the genetic algorithm"):
  with members:
    - "genetic algorithm based polar code construction for the awgn channel"
    - "design of polar codes in 5g new radio"
    - "mitigating clipping effects on error floors under belief propagation decoding of polar codes"
    - "polar code construction for list decoding"
    - "comparison of polar decoders with existing low density parity check and turbo decoders"
    - "minimum distance based construction of multi kernel polar codes"
    - "improving belief propagation decoding of polar codes using scattered exit charts"
    - "algebraic properties of polar codes from a new polynomial formalism"

Connected through hyperedge HE-7 (induced by source paper "deep learning based polar code design"):
  with members:
    - "decoder in the loop genetic optimization based ldpc code design"
    - "decoder tailored polar code design using the genetic algorithm"
    - "design of polar codes in 5g new radio"
    - "belief propagation decoding of polar codes on permuted factor graphs"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "sparse graphs for belief propagation decoding of polar codes"

Connected through hyperedge HE-8 (induced by source paper "optimizing polar codes compatible with off the shelf ldpc decoders"):
  with members:
    - "design of polar codes in 5g new radio"
    - "sparse graphs for belief propagation decoding of polar codes"
    - "code design for short blocks a survey"
    - "fast list decoders for polar codes"
    - "flexible length polar codes through graph based augmentation"
    - "a rm polar codes"
    - "from polar to reed muller codes a technique to improve the finite length performance"
    - "polar codes with dynamic frozen symbols and their decoding by directed search"

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)