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: "performance analysis of block markov superposition transmission of short codes"
  Abstract: In this paper, we consider the asymptotic and finite-length performance of block Markov superposition transmission~(BMST) of short codes, which can be viewed as a new class of spatially coupled~(SC) codes with the generator matrices of short codes~(referred to as {\em basic codes}) coupled. A modified extrinsic information transfer~(EXIT) chart analysis that takes into account the relation between mutual information~(MI) and bit-error-rate~(BER) is presented to study the convergence behavior of BMST codes. Using the modified EXIT chart analysis, we investigate the impact of various parameters on BMST code performance, thereby providing theoretical guidance for designing and implementing practical BMST codes suitable for sliding window decoding. Then, we present a performance comparison of BMST codes and SC low-density parity-check (SC-LDPC) codes on the basis of equal decoding latency. Also presented is a comparison of computational complexity. Simulation results show that, under the equal decoding latency constraint, BMST codes using the repetition code as the basic code can outperform $(3,6)$-regular SC-LDPC codes in the waterfall region but have a higher computational complexity.

Connected through hyperedge HE-1 (induced by source paper "statistical learning aided list decoding of semi random block oriented convolutional codes"):
  with members:
    - "decoding reed muller and polar codes by successive factor graph permutations"
    - "a new class of multiple rate codes based on block markov superposition transmission"
    - "block markov superposition transmission construction of big convolutional codes from short codes"
    - "list decoding of polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "statistical learning aided decoding of bmst of tail biting convolutional code"):
  with members:
    - "block markov superposition transmission of run codes"
    - "block markov superposition transmission construction of big convolutional codes from short codes"

Connected through hyperedge HE-3 (induced by source paper "block markov superposition transmission of bch codes with iterative erasures and errors decoders"):
  with members:
    - "block markov superposition transmission construction of big convolutional codes from short codes"
    - "staircase codes fec for 100 gb s otn"
    - "approaching capacity at high rates with iterative hard decision decoding"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-4 (induced by source paper "systematic block markov superposition transmission of repetition codes"):
  with members:
    - "a new class of multiple rate codes based on block markov superposition transmission"
    - "design of spatially coupled ldpc codes over gf q for windowed decoding"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "performance comparison of ldpc block and spatially coupled codes over gf q"
    - "block markov superposition transmission construction of big convolutional codes from short codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "lossy source coding via spatially coupled ldgm ensembles"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"

Connected through hyperedge HE-5 (induced by source paper "exit chart analysis of block markov superposition transmission of short codes"):
  with members:
    - "threshold saturation for nonbinary sc ldpc codes on the binary erasure channel"
    - "spatially coupled ldpc codes constructed from protographs"
    - "a new class of multiple rate codes based on block markov superposition transmission"
    - "design of spatially coupled ldpc codes over gf q for windowed decoding"
    - "performance comparison of ldpc block and spatially coupled codes over gf q"
    - "block markov superposition transmission construction of big convolutional codes from short codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"

Connected through hyperedge HE-6 (induced by source paper "block markov superposition transmission of run codes"):
  with members:
    - "exit chart analysis of block markov superposition transmission of short codes"
    - "a new class of multiple rate codes based on block markov superposition transmission"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "block markov superposition transmission construction of big convolutional codes from short codes"
    - "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"

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)