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: "the velocity of the decoding wave for spatially coupled codes on bms channels"
  Abstract: We consider the dynamics of belief propagation decoding of spatially coupled Low-Density Parity-Check codes. It has been conjectured that after a short transient phase, the profile of "error probabilities" along the spatial direction of a spatially coupled code develops a uniquely-shaped wave-like solution that propagates with constant velocity v. Under this assumption, and for transmission over general Binary Memoryless Symmetric channels, we derive a formula for v. We also propose approximations that are simpler to compute and support our findings using numerical data.

Connected through hyperedge HE-1 (induced by source paper "spatially coupled ldpc codes with non uniform coupling for improved decoding speed"):
  with members:
    - "avoiding burst like error patterns in windowed decoding of spatially coupled ldpc codes"
    - "non uniformly coupled ldpc codes better thresholds smaller rate loss and less complexity"
    - "spatially coupled ldpc codes constructed from protographs"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "windowed decoding of spatially coupled codes"

Connected through hyperedge HE-2 (induced by source paper "the velocity of the propagating wave for spatially coupled systems with applications to ldpc codes"):
  with members:
    - "analysis of coupled scalar systems by displacement convexity"
    - "the velocity of the propagating wave for general coupled scalar systems"
    - "finite length scaling based on belief propagation for spatially coupled ldpc codes"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "a simple proof of maxwell saturation for coupled scalar recursions"
    - "approaching the rate distortion limit with spatial coupling belief propagation and decimation"
    - "threshold saturation in spatially coupled constraint satisfaction problems"

Connected through hyperedge HE-3 (induced by source paper "the velocity of the propagating wave for general coupled scalar systems"):
  with members:
    - "analysis of coupled scalar systems by displacement convexity"
    - "a simple proof of maxwell saturation for coupled scalar recursions"
    - "threshold saturation in spatially coupled constraint satisfaction problems"
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "displacement convexity a useful framework for the study of spatially coupled codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "chains of mean field models"
    - "approaching capacity at high rates with iterative hard decision decoding"

Connected through hyperedge HE-4 (induced by source paper "non uniformly coupled ldpc codes better thresholds smaller rate loss and less complexity"):
  with members:
    - "spatially coupled ldpc codes constructed from protographs"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "efficient termination of spatially coupled codes"
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "windowed decoding of spatially coupled codes"
    - "threshold saturation on bms channels via spatial coupling"

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)