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: "a new class of multiple rate codes based on block markov superposition transmission"
  Abstract: Hadamard transform~(HT) as over the binary field provides a natural way to implement multiple-rate codes~(referred to as {\em HT-coset codes}), where the code length $N=2^p$ is fixed but the code dimension $K$ can be varied from $1$ to $N-1$ by adjusting the set of frozen bits. The HT-coset codes, including Reed-Muller~(RM) codes and polar codes as typical examples, can share a pair of encoder and decoder with implementation complexity of order $O(N \log N)$. However, to guarantee that all codes with designated rates perform well, HT-coset coding usually requires a sufficiently large code length, which in turn causes difficulties in the determination of which bits are better for being frozen. In this paper, we propose to transmit short HT-coset codes in the so-called block Markov superposition transmission~(BMST) manner. At the transmitter, signals are spatially coupled via superposition, resulting in long codes. At the receiver, these coupled signals are recovered by a sliding-window iterative soft successive cancellation decoding algorithm. Most importantly, the performance around or below the bit-error-rate~(BER) of $10^{-5}$ can be predicted by a simple genie-aided lower bound. Both these bounds and simulation results show that the BMST of short HT-coset codes performs well~(within one dB away from the corresponding Shannon limits) in a wide range of code rates.

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"
    - "performance analysis of block markov superposition transmission of short codes"
    - "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 "systematic block markov superposition transmission of repetition codes"):
  with members:
    - "performance analysis of block markov superposition transmission of short codes"
    - "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-3 (induced by source paper "performance 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"
    - "exit chart analysis of block markov superposition transmission of short codes"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "spatially coupled sparse codes on graphs theory and practice"
    - "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"

Connected through hyperedge HE-4 (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"
    - "performance analysis of block markov superposition transmission of short codes"
    - "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-5 (induced by source paper "block markov superposition transmission of run codes"):
  with members:
    - "exit chart analysis of block markov superposition transmission of short codes"
    - "performance analysis of block markov superposition transmission of short codes"
    - "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)