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 generalized algebraic approach to optimizing sc ldpc codes"
  Abstract: Spatially coupled low-density parity-check (SC-LDPC) codes are sparse graph codes that have recently become of interest due to their capacity-approaching performance on memoryless binary input channels. In this paper, we unify all existing SC-LDPC code construction methods under a new generalized description of SC-LDPC codes based on algebraic lifts of graphs. We present an improved low-complexity counting method for the special case of $(3,3)$-absorbing sets for array-based SC-LDPC codes, which we then use to optimize permutation assignments in SC-LDPC code construction. We show that codes constructed in this way are able to outperform previously published constructions, in terms of the number of dominant absorbing sets and with respect to both standard and windowed decoding.

Connected through hyperedge HE-1 (induced by source paper "a combinatorial methodology for optimizing non binary graph based codes theoretical analysis and applications in data storage"):
  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 ldpc codes constructed from protographs"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "iterative decoding of ldpc codes over the q ary partial erasure channel"
    - "thresholds of absorbing sets in low density parity check codes"

Connected through hyperedge HE-2 (induced by source paper "efficient search and elimination of harmful objects in optimized qc sc ldpc codes"):
  with members:
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "design and analysis of time invariant sc ldpc convolutional codes with small constraint length"
    - "spatially coupled ldpc codes constructed from protographs"
    - "on characterization of elementary trapping sets of variable regular ldpc codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-3 (induced by source paper "algebraic optimization of binary spatially coupled measurement matrices for interval passing"):
  with members:
    - "failure analysis of the interval passing algorithm for compressed sensing"
    - "on failing sets of the interval passing algorithm for compressed sensing"
    - "on the minimum distance of array based spatially coupled low density parity check codes"
    - "verification based interval passing algorithm for compressed sensing"
    - "a simple message passing algorithm for compressed sensing"
    - "the effect of spatial coupling on compressive sensing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"

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)