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: "neural belief propagation decoding of crc polar concatenated codes"
  Abstract: Polar codes are the first class of error correcting codes that provably achieve the channel capacity at infinite code length. They were selected for use in the fifth generation of cellular mobile communications (5G). In practical scenarios such as 5G, a cyclic redundancy check (CRC) is concatenated with polar codes to improve their finite length performance. This is mostly beneficial for sequential successive-cancellation list decoders. However, for parallel iterative belief propagation (BP) decoders, CRC is only used as an early stopping criterion with incremental error-correction performance improvement. In this paper, we first propose a CRC-polar BP (CPBP) decoder by exchanging the extrinsic information between the factor graph of the polar code and that of the CRC. We then propose a neural CPBP (NCPBP) algorithm which improves the CPBP decoder by introducing trainable normalizing weights on the concatenated factor graph. Our results on a 5G polar code of length 128 show that at the frame error rate of 10^(-5) and with a maximum of 30 iterations, the error-correction performance of CPBP and NCPBP are approximately 0.25 dB and 0.5 dB better than that of the conventional CRC-aided BP decoder, respectively, while introducing almost no latency overhead.

Connected through hyperedge HE-1 (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"
    - "belief propagation decoding of polar codes on permuted factor graphs"
    - "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-2 (induced by source paper "a gated hypernet decoder for polar codes"):
  with members:
    - "hyper graph network decoders for block codes"
    - "molecule property prediction and classification with graph hypernetworks"
    - "graph hypernetworks for neural architecture search"
    - "deep learning methods for improved decoding of linear codes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "on deep learning based channel decoding"
    - "smash one shot model architecture search through hypernetworks"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-3 (induced by source paper "deep learning aided successive cancellation decoding of polar codes"):
  with members:
    - "neural dynamic successive cancellation flip decoding of polar codes"
    - "improved bit flipping algorithm for successive cancellation decoding of polar codes"
    - "partitioned successive cancellation flip decoding of polar codes"
    - "deep learning methods for improved decoding of linear codes"
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "dynamic scflip decoding of polar codes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "llr based successive cancellation list decoding of polar codes"

Connected through hyperedge HE-4 (induced by source paper "neural dynamic successive cancellation flip decoding of polar codes"):
  with members:
    - "improved bit flipping algorithm for successive cancellation decoding of polar codes"
    - "partitioned successive cancellation flip decoding of polar codes"
    - "deep learning methods for improved decoding of linear codes"
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "dynamic scflip decoding of polar codes"
    - "neural offset min sum decoding"
    - "tensorflow a system for large scale machine learning"
    - "llr based successive cancellation 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)