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 "a combinatorial methodology for optimizing non binary graph based codes theoretical analysis and applications in data storage"):
  Abstract: Non-binary (NB) low-density parity-check (LDPC) codes are graph-based codes that are increasingly being considered as a powerful error correction tool for modern dense storage devices. Optimizing NB-LDPC codes to overcome their error floor is one of the main code design challenges facing storage engineers upon deploying such codes in practice. Furthermore, the increasing levels of asymmetry incorporated by the channels underlying modern dense storage systems, e.g., multi-level Flash systems, exacerbate the error floor problem by widening the spectrum of problematic objects that contribute to the error floor of an NB-LDPC code. In a recent research, the weight consistency matrix (WCM) framework was introduced as an effective combinatorial NB-LDPC code optimization methodology that is suitable for modern Flash memory and magnetic recording (MR) systems. The WCM framework was used to optimize codes for asymmetric Flash channels, MR channels that have intrinsic memory, in addition to canonical symmetric additive white Gaussian noise channels. In this paper, we provide an in-depth theoretical analysis needed to understand and properly apply the WCM framework. We focus on general absorbing sets of type two (GASTs) as the detrimental objects of interest. In particular, we introduce a novel tree representation of a GAST called the unlabeled GAST tree, using which we prove that the WCM framework is optimal in the sense that it operates on the minimum number of matrices, which are the WCMs, to remove a GAST. Then, we enumerate WCMs and demonstrate the significance of the savings achieved by the WCM framework in the number of matrices processed to remove a GAST. Moreover, we provide a linear-algebraic analysis of the null spaces of WCMs associated with a GAST. We derive the minimum number of edge weight changes needed to remove a GAST via its WCMs, along with how to choose these changes. In addition, we propose a new set of problematic objects, namely oscillating sets of type two (OSTs), which contribute to the error floor of NB-LDPC codes with even column weights on asymmetric channels, and we show how to customize the WCM framework to remove OSTs. We also extend the domain of the WCM framework applications by demonstrating its benefits in optimizing column weight 5 codes, codes used over Flash channels with additional soft information, and spatially coupled codes. The performance gains achieved via the WCM framework range between 1 and nearly 2.5 orders of magnitude in the error floor region over interesting channels.

Includes members:
  Member "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis and optimization of tail biting spatially coupled protograph ldpc codes for bicm id systems")
  Member "spatially coupled code design for partial response channels optimal object minimization approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient information reconciliation for energy time entanglement quantum key distribution")
  Member "a generalized algebraic approach to optimizing sc ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient search and elimination of harmful objects in optimized qc sc ldpc codes")
  Member "high performance non binary spatially coupled codes for flash memories":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "managing device lifecycle reconfigurable constrained codes for m t q p lc flash memories")
  Member "threshold saturation for nonbinary sc ldpc codes on the binary erasure channel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a finite length construction of irregular spatially coupled codes")
  Member "spatially coupled ldpc codes constructed from protographs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "a scaling law to predict the finite length performance of spatially coupled ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finite length scaling of spatially coupled ldpc codes under window decoding over the bec")
  Member "iterative decoding of ldpc codes over the q ary partial erasure channel":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "erasure correction of scalar codes in the presence of stragglers")

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)