human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "parity check polar coding for 5g and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polarization adjusted convolutional pac codes fano decoding vs list decoding")
  Member "deep learning methods for improved decoding of linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perm2vec graph permutation selection for decoding of error correction codes using self attention")
  Member "scaling deep learning based decoding of polar codes via partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning in the air")
  Member "on deep learning based channel decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "design of capacity approaching low density parity check codes using recurrent neural networks")
  Member "coding in the finite blocklength regime bounds based on laplace integrals and their asymptotic approximations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "resource allocation for secure multi user downlink miso urllc systems")
  Member "learning to decode linear codes using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "code design for short blocks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "decoder tailored polar code design using the genetic algorithm")
  Member "symbol decision successive cancellation list decoder for polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "polar codes and their quantum domain counterparts")

Question: Please tell me which class the source paper 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)