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 bounds of concatenated polar coding schemes"
  Abstract: A concatenated coding scheme over binary memoryless symmetric (BMS) channels using a polarization transformation followed by outer sub-codes is analyzed. Achievable error exponents and upper bounds on the error rate are derived. The first bound is obtained using outer codes which are typical linear codes from the ensemble of parity check matrices whose elements are chosen independently and uniformly. As a byproduct of this bound, it determines the required rate split of the total rate to the rates of the outer codes. A lower bound on the error exponent that holds for all BMS channels with a given capacity is then derived. Improved bounds and approximations for finite blocklength codes using channel dispersions (normal approximation), as well as converse and approximate converse results, are also obtained. The bounds are compared to actual simulation results from the literature. For the cases considered, when transmitting over the binary input additive white Gaussian noise channel, there was only a small gap between the normal approximation prediction and the actual error rate of concatenated BCH-polar codes.

Connected through hyperedge HE-1 (induced by source paper "list decoding of universal polar codes"):
  with members:
    - "polar codes for the deletion channel weak and strong polarization"
    - "universal polarization for processes with memory"
    - "soft decision decoding of reed muller codes recursive lists"
    - "fast polarization for processes with memory"
    - "polar coding for processes with memory"
    - "joint successive cancellation decoding of polar codes over intersymbol interference channels"
    - "polar codes with dynamic frozen symbols and their decoding by directed search"
    - "universal polar codes"

Connected through hyperedge HE-2 (induced by source paper "polar like codes and asymptotic tradeoff among block length code rate and error probability"):
  with members:
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "relaxed polar codes"
    - "minimum distance based construction of multi kernel polar codes"
    - "multi kernel polar codes proof of polarization and error exponents"
    - "a note on some inequalities used in channel polarization and polar coding"
    - "near optimal finite length scaling for polar codes over large alphabets"
    - "multi kernel construction of polar codes"
    - "flexible length polar codes through graph based augmentation"

Connected through hyperedge HE-3 (induced by source paper "deep learning methods for improved decoding of linear codes"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "rnn decoding of linear block codes"
    - "detection algorithms for communication systems using deep learning"
    - "neural offset min sum decoding"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"
    - "code design for short blocks a survey"

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)