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 "bp led decoding algorithm for ldpc codes over awgn channels"):
  Abstract: A new method for low-complexity near-maximum-likelihood (ML) decoding of low-density parity-check (LDPC) codes over the additive white Gaussian noise channel is presented. The proposed method termed belief-propagation--list erasure decoding (BP-LED) is based on erasing carefully chosen unreliable bits performed in case of BP decoding failure. A strategy of introducing erasures into the received vector and a new erasure decoding algorithm are proposed. The new erasure decoding algorithm, called list erasure decoding, combines ML decoding over the BEC with list decoding applied if the ML decoder fails to find a unique solution. The asymptotic exponent of the average list size for random regular LDPC codes from the Gallager ensemble is analyzed. Furthermore, a few examples of regular and irregular quasi-cyclic LDPC codes of short and moderate lengths are studied by simulations and their performance is compared with the upper bound on the LDPC ensemble-average performance and the upper bound on the average performance of random linear codes under ML decoding. A comparison with the BP decoding performance of the WiMAX standard codes and performance of the near-ML BEAST decoding are presented. The new algorithm is applied to decoding a short nonbinary LDPC code over the extension of the binary Galois field. The obtained simulation results are compared to the upper bound on the ensemble-average performance of the binary image of regular nonbinary LDPC codes.

Includes members:
  Member "new techniques for upper bounding the ml decoding performance of binary linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enumeration of minimum hamming weight polar codewords with sublinear complexity")
  Member "weight distributions of regular low density parity check codes over finite fields":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distance verification for classical and quantum ldpc codes")
  Member "tree structure expectation propagation for decoding ldpc codes over binary erasure channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distance properties of short ldpc codes and their impact on the bp ml and near ml decoding performance")
  Member "lowering the error floor of ldpc codes using cyclic liftings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "integer ring sieve irs for constructing compact qc ldpc codes with large girth")
  Member "tightened upper bounds on the ml decoding error probability of binary linear block codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the renyi divergence joint range of relative entropies and a channel coding theorem")

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)