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: "band splitting permutations for spatially coupled ldpc codes enhancing burst erasure immunity"
  Abstract: It is well known that spatially coupled (SC) codes with erasure-BP decoding have powerful error correcting capability over memoryless erasure channels. However, the decoding performance of SC-codes significantly degrades when they are used over burst erasure channels. In this paper, we propose band splitting permutations (BSP) suitable for $(l,r,L)$ SC-codes. The BSP splits a diagonal band in a base matrix into multiple bands in order to enhance the span of the stopping sets in the base matrix. As theoretical performance guarantees, lower and upper bounds on the maximal burst correctable length of the permuted $(l,r,L)$ SC-codes are presented. Those bounds indicate that the maximal correctable burst ratio of the permuted SC-codes converges to 1/k where k=r/l. This implies the asymptotic optimality of the permuted SC-codes in terms of burst erasure correction.

Connected through hyperedge HE-1 (induced by source paper "spatially coupled ldpc ensembles on burst erasure channels"):
  with members:
    - "spatially coupled ldpc codes affected by a single random burst of erasures"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "scaling behavior of convolutional ldpc ensembles over the bec"
    - "low density parity check codes for nonergodic block fading channels"

Connected through hyperedge HE-2 (induced by source paper "finite length analysis of spatially coupled regular ldpc ensembles on burst erasure channels"):
  with members:
    - "spatially coupled ldpc codes affected by a single random burst of erasures"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "spatially coupled random access on graphs"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "scaling behavior of convolutional ldpc ensembles over the bec"

Connected through hyperedge HE-3 (induced by source paper "spatially coupled ldpc codes affected by a single random burst of erasures"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "low density parity check codes for nonergodic block fading channels"

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)