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: "scaling behavior of convolutional ldpc ensembles over the bec"
  Abstract: We study the scaling behavior of coupled sparse graph codes over the binary erasure channel. In particular, let 2L+1 be the length of the coupled chain, let M be the number of variables in each of the 2L + 1 local copies, let l be the number of iterations, let Pb denote the bit error probability, and let {\epsilon} denote the channel parameter. We are interested in how these quantities scale when we let the blocklength (2L + 1)M tend to infinity. Based on empirical evidence we show that the threshold saturation phenomenon is rather stable with respect to the scaling of the various parameters and we formulate some general rules of thumb which can serve as a guide for the design of coding systems based on coupled graphs.

Connected through hyperedge HE-1 (induced by source paper "covariance evolution for spatially mt fuji coupled ldpc codes"):
  with members:
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-2 (induced by source paper "spatially coupled ldpc ensembles on burst erasure channels"):
  with members:
    - "spatially coupled ldpc codes affected by a single random burst of erasures"
    - "band splitting permutations for spatially coupled ldpc codes enhancing burst erasure immunity"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "low density parity check codes for nonergodic block fading channels"

Connected through hyperedge HE-3 (induced by source paper "finite length analysis of spatially coupled regular ldpc ensembles on burst erasure channels"):
  with members:
    - "spatially coupled ldpc codes affected by a single random burst of erasures"
    - "band splitting permutations for spatially coupled ldpc codes enhancing burst erasure immunity"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "spatially coupled random access on graphs"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-4 (induced by source paper "spatially coupled ldpc codes constructed from protographs"):
  with members:
    - "performance comparison of ldpc block and spatially coupled codes over gf q"
    - "deterministic constructions for large girth protograph ldpc codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "lossy source coding via spatially coupled ldgm ensembles"
    - "spatially coupled random access on graphs"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "threshold saturation on channels with memory via spatial coupling"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"

Connected through hyperedge HE-5 (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"
    - "on the minimum distance of generalized spatially coupled ldpc codes"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "improving the finite length performance of spatially coupled ldpc codes by connecting multiple code chains"):
  with members:
    - "new codes on graphs constructed by connecting spatially coupled chains"
    - "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"
    - "connecting spatially coupled ldpc code chains"
    - "improvement of bp based cdma multiuser detection by spatial coupling"

Connected through hyperedge HE-7 (induced by source paper "block markov superposition transmission construction of big convolutional codes from short codes"):
  with members:
    - "on the minimum distance of generalized 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"
    - "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"
    - "windowed decoding of spatially coupled codes"
    - "deriving good ldpc convolutional codes from ldpc block codes"

Connected through hyperedge HE-8 (induced by source paper "linear programming decoding of spatially coupled codes"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "probabilistic analysis of linear programming decoding"
    - "towards low complexity linear programming decoding"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc 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)