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 of spatially coupled ldpc codes and threshold saturation over bicm channels"
  Abstract: We study the performance of binary spatially-coupled low-density parity-check codes (SC-LDPC) when used with bit-interleaved coded-modulation (BICM) schemes. This paper considers the cases when transmission takes place over additive white Gaussian noise (AWGN)channels and Rayleigh fast-fading channels. The technique of upper bounding the maximum-a-posteriori (MAP) decoding performance of LDPC codes using an area theorem is extended for BICM schemes. The upper bound is computed for both the optimal MAP demapper and the suboptimal max-log-MAP (MLM) demapper. It is observed that this bound approaches the noise threshold of BICM channels for regular LDPC codes with large degrees. The rest of the paper extends these techniques to SC-LDPC codes and the phenomenon of threshold saturation is demonstrated numerically. Based on numerical evidence, we conjecture that the belief-propagation (BP) decoding threshold of SC-LDPC codes approaches the MAP decoding threshold of the underlying LDPC ensemble on BICM channels. Numerical results also show that SC-LDPC codes approach the BICM capacity over different channels and modulation schemes.

Connected through hyperedge HE-1 (induced by source paper "triggering wave like convergence of tail biting spatially coupled ldpc codes"):
  with members:
    - "optimized bit mappings for spatially coupled ldpc codes over parallel binary erasure channels"
    - "a generalization of threshold saturation application to spatially coupled bicm id"
    - "terminated and tailbiting spatially coupled codes with optimized bit mappings for spectrally efficient fiber optical systems"
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "threshold saturation on bms channels via spatial coupling"

Connected through hyperedge HE-2 (induced by source paper "optimized bit mappings for spatially coupled ldpc codes over parallel binary erasure channels"):
  with members:
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "threshold saturation on bms channels via spatial coupling"
    - "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)