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: "asymptotically regular ldpc codes with linear distance growth and thresholds close to capacity"
  Abstract: Families of "asymptotically regular" LDPC block code ensembles can be formed by terminating (J,K)-regular protograph-based LDPC convolutional codes. By varying the termination length, we obtain a large selection of LDPC block code ensembles with varying code rates and substantially better iterative decoding thresholds than those of (J,K)-regular LDPC block code ensembles, despite the fact that the terminated ensembles are almost regular. Also, by means of an asymptotic weight enumerator analysis, we show that minimum distance grows linearly with block length for all of the ensembles in these families, i.e., the ensembles are asymptotically good. We find that, as the termination length increases, families of "asymptotically regular" codes with capacity approaching iterative decoding thresholds and declining minimum distance growth rates are obtained, allowing a code designer to trade-off between distance growth rate and threshold. Further, we show that the thresholds and the distance growth rates can be improved by carefully choosing the component protographs used in the code construction.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "spatially coupled codes and optical fiber communications an ideal match"):
  with members:
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "staircase codes fec for 100 gb s otn"
    - "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 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-5 (induced by source paper "spatially coupled ldpc codes for decode and forward relaying of two correlated sources over the bec"):
  with members:
    - "source channel coding theorems for the multiple access relay channel"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "universality for the noisy slepian wolf problem via spatial coupling"
    - "bilayer ldpc convolutional codes for half duplex relay channels"
    - "ldpc code design for transmission of correlated sources across noisy channels without csit"
    - "can iterative decoding for erasure correlated sources be universal"
    - "bilayer low density parity check codes for decode and forward in relay channels"
    - "network information flow with correlated sources"

Connected through hyperedge HE-6 (induced by source paper "approaching the rate distortion limit with spatial coupling belief propagation and decimation"):
  with members:
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "threshold saturation in spatially coupled constraint satisfaction problems"
    - "spatial coupling as a proof technique"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "lossy source coding via spatially coupled ldgm ensembles"
    - "chains of mean field models"
    - "fixed length lossy compression in the finite blocklength regime"

Connected through hyperedge HE-7 (induced by source paper "new codes on graphs constructed by connecting spatially coupled chains"):
  with members:
    - "a proof of threshold saturation for spatially coupled ldpc codes on bms channels"
    - "spatially coupled quasi cyclic quantum ldpc codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "universality for the noisy slepian wolf problem via spatial coupling"
    - "multiple access demodulation in the lifted signal graph with spatial coupling"
    - "coupled graphical models and their thresholds"
    - "bilayer ldpc convolutional codes for half duplex relay channels"
    - "improvement of bp based cdma multiuser detection by spatial coupling"

Connected through hyperedge HE-8 (induced by source paper "on the convergence speed of spatially coupled ldpc ensembles"):
  with members:
    - "a proof of threshold saturation for spatially coupled ldpc codes on bms channels"
    - "spatial coupling as a proof technique"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "wave like solutions of general one dimensional spatially coupled systems"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "windowed decoding of spatially coupled codes"
    - "terminated ldpc convolutional codes with thresholds close to capacity"

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)