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: "genetic algorithm based polar code construction for the awgn channel"
  Abstract: We propose a new polar code construction framework (i.e., selecting the frozen bit positions) for the additive white Gaussian noise (AWGN) channel, tailored to a given decoding algorithm, rather than based on the (not necessarily optimal) assumption of successive cancellation (SC) decoding. The proposed framework is based on the Genetic Algorithm (GenAlg), where populations (i.e., collections) of information sets evolve successively via evolutionary transformations based on their individual error-rate performance. These populations converge towards an information set that fits the decoding behavior. Using our proposed algorithm, we construct a polar code of length 2048 with code rate 0.5, without the CRC-aid, tailored to plain successive cancellation list (SCL) decoding, achieving the same error-rate performance as the CRC-aided SCL decoding, and leading to a coding gain of 1 dB at BER of $10^{-6}$. Further, a belief propagation (BP)-tailored polar code approaches the SCL error-rate performance without any modifications in the decoding algorithm itself.

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"
    - "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"
    - "dynamic scflip decoding of polar codes"

Connected through hyperedge HE-2 (induced by source paper "decoder tailored polar code design using the genetic algorithm"):
  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"
    - "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"
    - "algebraic properties of polar codes from a new polynomial formalism"

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)