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: "on the minimum distance of generalized spatially coupled ldpc codes"
  Abstract: Families of generalized spatially-coupled low-density parity-check (GSC-LDPC) code ensembles can be formed by terminating protograph-based generalized LDPC convolutional (GLDPCC) codes. It has previously been shown that ensembles of GSC-LDPC codes constructed from a protograph have better iterative decoding thresholds than their block code counterparts, and that, for large termination lengths, their thresholds coincide with the maximum a-posteriori (MAP) decoding threshold of the underlying generalized LDPC block code ensemble. Here we show that, in addition to their excellent iterative decoding thresholds, ensembles of GSC-LDPC codes are asymptotically good and have large minimum distance growth rates.

Connected through hyperedge HE-1 (induced by source paper "spatially coupled generalized ldpc codes asymptotic analysis and finite length scaling"):
  with members:
    - "a refined scaling law for spatially coupled ldpc codes over the binary erasure channel"
    - "spatially coupled ldpc codes constructed from protographs"
    - "design of ldpc code ensembles with fast convergence properties"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "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"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"

Connected through hyperedge HE-2 (induced by source paper "on generalized ldpc codes for 5g ultra reliable communication"):
  with members:
    - "a probabilistic peeling decoder to efficiently analyze generalized ldpc codes over the bec"
    - "ultra reliable communication in 5g wireless systems"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-3 (induced by source paper "a probabilistic peeling decoder to efficiently analyze generalized ldpc codes over the bec"):
  with members:
    - "design of ldpc code ensembles with fast convergence properties"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "spectral shape of doubly generalized ldpc codes efficient and exact evaluation"
    - "approaching capacity at high rates with iterative hard decision decoding"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-4 (induced by source paper "a scaling law to predict the finite length performance of spatially coupled ldpc codes"):
  with members:
    - "spatially coupled sparse codes on graphs theory and practice"
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "analytical solution of covariance evolution for irregular ldpc codes"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "exact free distance and trapping set growth rates for ldpc convolutional codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "scaling behavior of convolutional ldpc ensembles over the bec"

Connected through hyperedge HE-5 (induced by source paper "block markov superposition transmission construction of big convolutional codes from short codes"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "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"
    - "new techniques for upper bounding the ml decoding performance of binary linear codes"
    - "serial concatenation of rs codes with kite codes performance analysis iterative decoding and design"
    - "scaling behavior of convolutional ldpc ensembles over the bec"
    - "windowed decoding of spatially coupled codes"
    - "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)