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: "deriving good ldpc convolutional codes from ldpc block codes"
  Abstract: Low-density parity-check (LDPC) convolutional codes are capable of achieving excellent performance with low encoding and decoding complexity. In this paper we discuss several graph-cover-based methods for deriving families of time-invariant and time-varying LDPC convolutional codes from LDPC block codes and show how earlier proposed LDPC convolutional code constructions can be presented within this framework. Some of the constructed convolutional codes significantly outperform the underlying LDPC block codes. We investigate some possible reasons for this "convolutional gain," and we also discuss the --- mostly moderate --- decoder cost increase that is incurred by going from LDPC block to LDPC convolutional codes.

Connected through hyperedge HE-1 (induced by source paper "analysis and optimization of tail biting spatially coupled protograph ldpc codes for bicm id systems"):
  with members:
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"
    - "improving the decoding threshold of tailbiting spatially coupled ldpc codes by energy shaping"
    - "compact qc ldpc block and sc ldpc convolutional codes for low latency communications"
    - "design and analysis of time invariant sc ldpc convolutional codes with small constraint length"
    - "wave like decoding of tail biting spatially coupled ldpc codes through iterative demapping"
    - "finite length analysis of spatially coupled regular ldpc ensembles on burst erasure channels"
    - "efficient multi dimensional mapping using qam constellations for bicm id"

Connected through hyperedge HE-2 (induced by source paper "construction of rate n 1 n non binary ldpc convolutional codes via difference triangle sets"):
  with members:
    - "constructions for difference triangle sets"

Connected through hyperedge HE-3 (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"
    - "a generalized algebraic approach to optimizing sc ldpc codes"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a channel aware combinatorial approach to design high performance spatially coupled codes for magnetic recording systems"):
  with members:
    - "a combinatorial methodology for optimizing non binary graph based codes theoretical analysis and applications in data storage"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "design and analysis of time invariant sc ldpc convolutional codes with small constraint length"):
  with members:
    - "on advisability of designing short length qc ldpc codes using perfect difference families"
    - "spatially coupled ldpc codes constructed from protographs"
    - "construction of high rate regular quasi cyclic ldpc codes based on cyclic difference families"
    - "quasi cyclic ldpc codes influence of proto and tanner graph structure on minimum hamming distance upper bounds"
    - "progressive differences convolutional low density parity check codes"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "terminated ldpc convolutional codes with thresholds close to capacity"

Connected through hyperedge HE-6 (induced by source paper "spatially coupled code design for partial response channels optimal object minimization approach"):
  with members:
    - "a combinatorial methodology for optimizing non binary graph based codes theoretical analysis and applications in data storage"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "design and analysis of time invariant sc ldpc codes with small constraint length"):
  with members:
    - "on advisability of designing short length qc ldpc codes using perfect difference families"
    - "spatially coupled ldpc codes constructed from protographs"
    - "hierarchical and high girth qc ldpc codes"
    - "construction of high rate regular quasi cyclic ldpc codes based on cyclic difference families"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "searching for voltage graph based ldpc tailbiting codes with large girth"
    - "progressive differences convolutional low density parity check codes"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"

Connected through hyperedge HE-8 (induced by source paper "high performance non binary spatially coupled codes for flash memories"):
  with members:
    - "a combinatorial methodology for optimizing non binary graph based codes theoretical analysis and applications in data storage"
    - "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"

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)