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 "systematic convolutional low density generator matrix code"):
  Abstract: In this paper, we propose a systematic low density generator matrix (LDGM) code ensemble, which is defined by the Bernoulli process. We prove that, under maximum likelihood (ML) decoding, the proposed ensemble can achieve the capacity of binary-input output symmetric (BIOS) memoryless channels in terms of bit error rate (BER). The proof technique reveals a new mechanism, different from lowering down frame error rate (FER), that the BER can be lowered down by assigning light codeword vectors to light information vectors. The finite length performance is analyzed by deriving an upper bound and a lower bound, both of which are shown to be tight in the high signal-to-noise ratio (SNR) region. To improve the waterfall performance, we construct the systematic convolutional LDGM (SC-LDGM) codes by a random splitting process. The SC-LDGM codes are easily configurable in the sense that any rational code rate can be realized without complex optimization. As a universal construction, the main advantage of the SC-LDGM codes is their near-capacity performance in the waterfall region and predictable performance in the error-floor region that can be lowered down to any target as required by increasing the density of the uncoupled LDGM codes. Numerical results are also provided to verify our analysis.

Includes members:
  Member "systematic block markov superposition transmission of repetition codes":
    also connected through other hyperedges:
      (none)
  Member "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "block markov superposition transmission construction of big convolutional codes from short codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "spatially coupled ensembles universally achieve capacity under belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "threshold saturation on bms channels via spatial coupling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "design of irregular sc ldpc codes with non uniform degree distributions by linear programing")
  Member "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reed muller codes theory and algorithms")

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)