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: "awgn channel analysis of terminated ldpc convolutional codes"
  Abstract: It has previously been shown that ensembles of terminated protograph-based low-density parity-check (LDPC) convolutional codes have a typical minimum distance that grows linearly with block length and that they are capable of achieving capacity approaching iterative decoding thresholds on the binary erasure channel (BEC). In this paper, we review a recent result that the dramatic threshold improvement obtained by terminating LDPC convolutional codes extends to the additive white Gaussian noise (AWGN) channel. Also, using a (3,6)-regular protograph-based LDPC convolutional code ensemble as an example, we perform an asymptotic trapping set analysis of terminated LDPC convolutional code ensembles. In addition to capacity approaching iterative decoding thresholds and linearly growing minimum distance, we find that the smallest non-empty trapping set of a terminated ensemble grows linearly with block length.

Connected through hyperedge HE-1 (induced by source paper "spatially coupled ldpc codes with sub block locality"):
  with members:
    - "ldpc codes with local and global decoding"
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "multi block interleaved codes for local and global read access"
    - "integrated interleaved codes as locally recoverable codes properties and performance"
    - "spatially coupled ldpc codes constructed from protographs"
    - "a family of optimal locally recoverable codes"
    - "multi dimensional spatially coupled codes"
    - "new codes on graphs constructed by connecting spatially coupled chains"

Connected through hyperedge HE-2 (induced by source paper "advances in detection and error correction for coherent optical communications regular irregular and spatially coupled ldpc code designs"):
  with members:
    - "spatially coupled sparse codes on graphs theory and practice"
    - "improving soft fec performance for higher order modulations via optimized bit channel mappings"
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "staircase codes fec for 100 gb s otn"
    - "analysis and design of binary message passing decoders"

Connected through hyperedge HE-3 (induced by source paper "terminated and tailbiting spatially coupled codes with optimized bit mappings for spectrally efficient fiber optical systems"):
  with members:
    - "improving soft fec performance for higher order modulations via optimized bit channel mappings"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "staircase codes fec for 100 gb s otn"
    - "approaching capacity at high rates with iterative hard decision decoding"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-4 (induced by source paper "improving soft fec performance for higher order modulations via optimized bit channel mappings"):
  with members:
    - "optimized bit mappings for spatially coupled ldpc codes over parallel binary erasure channels"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

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)