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: "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.

Connected through hyperedge HE-1 (induced by source paper "minimizing the number of detrimental objects in multi dimensional graph based codes"):
  with members:
    - "multi dimensional spatially coupled code design through informed relocation of circulants"
    - "high performance non binary spatially coupled codes for flash memories"
    - "new codes on graphs constructed by connecting spatially coupled chains"

Connected through hyperedge HE-2 (induced by source paper "a channel aware combinatorial approach to design high performance spatially coupled codes for magnetic recording systems"):
  with members:
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "spatially coupled code design for partial response channels optimal object minimization approach"
    - "high performance non binary spatially coupled codes for flash memories"
    - "threshold saturation for nonbinary sc ldpc codes on the binary erasure channel"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "deriving good ldpc convolutional codes from ldpc block codes"

Connected through hyperedge HE-3 (induced by source paper "spatially coupled code design for partial response channels optimal object minimization approach"):
  with members:
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "high performance non binary spatially coupled codes for flash memories"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "deriving good ldpc convolutional codes from ldpc block codes"

Connected through hyperedge HE-4 (induced by source paper "high performance non binary spatially coupled codes for flash memories"):
  with members:
    - "design of non binary quasi cyclic ldpc codes by ace optimization"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "deriving good ldpc convolutional codes from ldpc block codes"

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)