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: "sparse graphs for belief propagation decoding of polar codes"
  Abstract: We describe a novel approach to interpret a polar code as a low-density parity-check (LDPC)-like code with an underlying sparse decoding graph. This sparse graph is based on the encoding factor graph of polar codes and is suitable for conventional belief propagation (BP) decoding. We discuss several pruning techniques based on the check node decoder (CND) and variable node decoder (VND) update equations, significantly reducing the size (i.e., decoding complexity) of the parity-check matrix. As a result, iterative polar decoding can then be conducted on a sparse graph, akin to the traditional well-established LDPC decoding (e.g., using a fully parallel sum product algorithm (SPA)). We show that the proposed iterative polar decoder has a negligible performance loss for short-to-intermediate codelengths compared to Ar{\i}kan's original BP decoder; whereas we observed that this loss diminishes for more BP iterations. Finally, the proposed decoder is shown to benefit from a reduced complexity and reduced memory requirements compared to Ar{\i}kan's original BP decoder, thus it is more suitable for hardware implementations.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "optimizing polar codes compatible with off the shelf ldpc decoders"):
  with members:
    - "design of polar codes in 5g new radio"
    - "belief propagation list 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"

Connected through hyperedge HE-3 (induced by source paper "decoder in the loop genetic optimization based ldpc code design"):
  with members:
    - "decoder tailored polar code design using the genetic algorithm"
    - "efficient error correcting codes in the short blocklength regime"
    - "scattered exit charts for finite length ldpc code design"
    - "on the stopping distance and the stopping redundancy of codes"

Connected through hyperedge HE-4 (induced by source paper "design of polar codes in 5g new radio"):
  with members:
    - "on list decoding of 5g nr polar codes"
    - "parity check polar coding for 5g and beyond"
    - "design and implementation of a polar codes blind detection scheme"
    - "circular buffer rate matched polar codes"
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "partitioned list decoding of polar codes analysis and improvement of finite length performance"
    - "on the error probability of short concatenated polar and cyclic codes with interleaving"
    - "on puncturing strategies 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)