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: "performance of polar codes for channel and source coding"
  Abstract: Polar codes, introduced recently by Ar\i kan, are the first family of codes known to achieve capacity of symmetric channels using a low complexity successive cancellation decoder. Although these codes, combined with successive cancellation, are optimal in this respect, their finite-length performance is not record breaking. We discuss several techniques through which their finite-length performance can be improved. We also study the performance of these codes in the context of source coding, both lossless and lossy, in the single-user context as well as for distributed applications.

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"
    - "belief propagation list decoding of polar codes"

Connected through hyperedge HE-2 (induced by source paper "multi kernel polar codes concept and design principles"):
  with members:
    - "design of polar codes in 5g new radio"
    - "low complexity puncturing and shortening of polar codes"
    - "on the successive cancellation decoding of polar codes with arbitrary linear binary kernels"
    - "minimum distance based construction of multi kernel polar codes"
    - "multi kernel polar codes proof of polarization and error exponents"
    - "multi kernel construction of polar codes"
    - "binary polarization kernels from code decompositions"
    - "from polar to reed muller codes a technique to improve the finite length performance"

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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "partially symmetric monomial codes"):
  with members:
    - "permutation decoding of polar codes"
    - "recursive projection aggregation decoding of reed muller codes"
    - "a new permutation decoding method for reed muller codes"
    - "soft decision decoding of reed muller codes recursive lists"
    - "algebraic properties of polar codes from a new polynomial formalism"
    - "reed muller codes achieve capacity on erasure channels"
    - "list decoding of polar codes"

Connected through hyperedge HE-5 (induced by source paper "polar codes simplicity random codes durability"):
  with members:
    - "reed muller codes polarize"
    - "ar i kan meets shannon polar codes with near optimal convergence to channel capacity"
    - "explicit polar codes with small scaling exponent"
    - "on the capacity of channels with deletions and states"
    - "polar codes for the deletion channel weak and strong polarization"
    - "polar like codes and asymptotic tradeoff among block length code rate and error probability"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "relaxed 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"
    - "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-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"
    - "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-8 (induced by source paper "sign bit shaping using polar codes"):
  with members:
    - "probabilistically shaped multi level coding with polar codes for fading channels"
    - "code constructions for physical unclonable functions and biometric secrecy systems"
    - "efficient polar code construction for higher order modulation"
    - "polar codes for broadcast channels"
    - "llr based successive cancellation list decoding of polar codes"
    - "how to achieve the capacity of asymmetric channels"
    - "achievable rates for shaped bit metric decoding"
    - "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)