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 decoding of polar codes on permuted factor graphs"
  Abstract: We show that the performance of iterative belief propagation (BP) decoding of polar codes can be enhanced by decoding over different carefully chosen factor graph realizations. With a genie-aided stopping condition, it can achieve the successive cancellation list (SCL) decoding performance which has already been shown to achieve the maximum likelihood (ML) bound provided that the list size is sufficiently large. The proposed decoder is based on different realizations of the polar code factor graph with randomly permuted stages during decoding. Additionally, a different way of visualizing the polar code factor graph is presented, facilitating the analysis of the underlying factor graph and the comparison of different graph permutations. In our proposed decoder, a high rate Cyclic Redundancy Check (CRC) code is concatenated with a polar code and used as an iteration stopping criterion (i.e., genie) to even outperform the SCL decoder of the plain polar code (without the CRC-aid). Although our permuted factor graph-based decoder does not outperform the SCL-CRC decoder, it achieves, to the best of our knowledge, the best performance of all iterative polar decoders presented thus far.

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"
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "belief propagation list decoding of polar codes"
    - "dynamic scflip decoding of polar codes"

Connected through hyperedge HE-2 (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 list decoding of polar codes"
    - "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-3 (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 list decoding of polar codes"
    - "a comparative study of polar code constructions for the awgn channel"
    - "list decoding of polar codes"

Connected through hyperedge HE-4 (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"
    - "end to end learning of communications systems without a channel model"
    - "belief propagation list decoding of polar codes"
    - "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-5 (induced by source paper "partially permuted multi trellis belief propagation for polar codes"):
  with members:
    - "on the decoding of polar codes on permuted factor graphs"
    - "belief propagation list decoding of polar codes"
    - "mitigating clipping effects on error floors under belief propagation decoding of polar codes"
    - "partitioned successive cancellation list decoding of polar codes"
    - "list decoding of polar codes"
    - "on finite length performance of polar codes stopping sets error floor and concatenated design"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "performance of polar codes for channel and source coding"

Connected through hyperedge HE-6 (induced by source paper "on the decoding of polar codes on permuted factor graphs"):
  with members:
    - "partitioned list decoding of polar codes analysis and improvement of finite length performance"
    - "fast and flexible successive cancellation list decoders for polar codes"
    - "blind detection with polar codes"
    - "llr based successive cancellation list decoding of polar codes"
    - "list decoding of polar codes"
    - "performance of polar codes for channel and source coding"

Connected through hyperedge HE-7 (induced by source paper "decoding reed muller and polar codes by successive factor graph permutations"):
  with members:
    - "on the decoding of polar codes on permuted factor graphs"
    - "soft decision decoding of reed muller codes recursive lists"
    - "recursive decoding and its performance for low rate reed muller codes"
    - "llr based successive cancellation list decoding of polar codes"
    - "list decoding of polar codes"

Connected through hyperedge HE-8 (induced by source paper "neural belief propagation decoding of crc polar concatenated codes"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "neural offset min sum decoding"
    - "improving belief propagation decoding of polar codes using scattered exit charts"
    - "list decoding of polar codes"

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)