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 good ldpc convolutional codes based on protographs"
  Abstract: LDPC convolutional codes have been shown to be capable of achieving the same capacity-approaching performance as LDPC block codes with iterative message-passing decoding. In this paper, asymptotic methods are used to calculate a lower bound on the free distance for several ensembles of asymptotically good protograph-based LDPC convolutional codes. Further, we show that the free distance to constraint length ratio of the LDPC convolutional codes exceeds the minimum distance to block length ratio of corresponding LDPC block codes.

Connected through hyperedge HE-1 (induced by source paper "spatially coupled ensembles universally achieve capacity under belief propagation"):
  with members:
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "universal bounds on the scaling behavior of polar codes"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "chains of mean field models"
    - "a phenomenological study on threshold improvement via spatial coupling"
    - "wave like solutions of general one dimensional spatially coupled systems"
    - "extended extremes of information combining"
    - "threshold saturation on channels with memory via spatial coupling"

Connected through hyperedge HE-2 (induced by source paper "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"):
  with members:
    - "threshold saturation on bms channels via spatial coupling"
    - "new families of ldpc block codes formed by terminating irregular protograph based ldpc convolutional codes"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "the generalized area theorem and some of its consequences"
    - "terminated ldpc convolutional codes with thresholds close to capacity"
    - "life above threshold from list decoding to area theorem and mse"

Connected through hyperedge HE-3 (induced by source paper "exact free distance and trapping set growth rates for ldpc convolutional codes"):
  with members:
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "deriving good ldpc convolutional codes from ldpc block codes"
    - "free distance bounds for protograph based regular ldpc convolutional codes"

Connected through hyperedge HE-4 (induced by source paper "the effect of spatial coupling on compressive sensing"):
  with members:
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "coupled graphical models and their thresholds"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "threshold saturation on bms channels via spatial coupling"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"
    - "on the iterative decoding of high rate ldpc codes with applications in compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "quasi cyclic asymptotically regular ldpc codes"):
  with members:
    - "quasi cyclic ldpc codes influence of proto and tanner graph structure on minimum hamming distance upper bounds"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "asymptotically regular ldpc codes with linear distance growth and thresholds close to capacity"

Connected through hyperedge HE-6 (induced by source paper "new families of ldpc block codes formed by terminating irregular protograph based ldpc convolutional codes"):
  with members:
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "asymptotically regular ldpc codes with linear distance growth and thresholds close to capacity"

Connected through hyperedge HE-7 (induced by source paper "asymptotically regular ldpc codes with linear distance growth and thresholds close to capacity"):
  with members:
    - "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)