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: "combining belief propagation and successive cancellation list decoding of polar codes on a gpu platform"
  Abstract: The decoding performance of polar codes strongly depends on the decoding algorithm used, while also the decoder throughput and its latency mainly depend on the decoding algorithm. In this work, we implement the powerful successive cancellation list (SCL) decoder on a GPU and identify the bottlenecks of this algorithm with respect to parallel computing and its difficulties. The inherent serial decoding property of the SCL algorithm naturally limits the achievable speed-up gains on GPUs when compared to CPU implementations. In order to increase the decoding throughput, we use a hybrid decoding scheme based on the belief propagation (BP) decoder, which can be intra and inter-frame parallelized. The proposed scheme combines excellent decoding performance and high throughput within the signal-to-noise ratio (SNR) region of interest.

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"
    - "neural belief propagation decoding of crc polar concatenated codes"
    - "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"
    - "flexible length polar codes through graph based augmentation"

Connected through hyperedge HE-2 (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"
    - "belief propagation list decoding of polar codes"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "genetic algorithm based polar code construction for the awgn channel"):
  with members:
    - "design of polar codes in 5g new radio"
    - "belief propagation list decoding of polar codes"
    - "mitigating clipping effects on error floors under belief propagation decoding of polar codes"
    - "polar code construction for list decoding"
    - "a comparative study of polar code constructions for the awgn channel"
    - "a rm polar codes"
    - "from polar to reed muller codes a technique to improve the finite length performance"
    - "list decoding of polar codes"

Connected through hyperedge HE-4 (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"
    - "rnn decoding of linear block codes"
    - "partitioned successive cancellation list decoding of polar codes"
    - "improving belief propagation decoding of polar codes using scattered exit charts"
    - "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-5 (induced by source paper "mitigating clipping effects on error floors under belief propagation decoding of polar codes"):
  with members:
    - "comparison of polar decoders with existing low density parity check and turbo decoders"
    - "on deep learning based channel decoding"
    - "improving belief propagation decoding of polar codes using scattered exit charts"
    - "flexible length polar codes through graph based augmentation"
    - "reduced complexity belief propagation decoders for polar codes"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "the effect of saturation on belief propagation decoding of ldpc codes"
    - "list decoding of polar codes"

Connected through hyperedge HE-6 (induced by source paper "scaling deep learning based decoding of polar codes via partitioning"):
  with members:
    - "partitioned successive cancellation list decoding of polar codes"
    - "learning to decode linear codes using deep learning"
    - "hardware decoders for polar codes an overview"
    - "code design for short blocks a survey"
    - "fast list decoders for polar codes"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "xj bp express journey belief propagation decoding for 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)