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: "improving the decoding threshold of tailbiting spatially coupled ldpc codes by energy shaping"
  Abstract: We show how the iterative decoding threshold of tailbiting spatially coupled (SC) low-density parity-check (LDPC) code ensembles can be improved over the binary input additive white Gaussian noise channel by allowing the use of different transmission energies for the codeword bits. We refer to the proposed approach as  energy shaping . We focus on the special case where the transmission energy of a bit is selected among two values, and where a contiguous portion of the codeword is transmitted with the largest one. Given these constraints, an optimal  energy boosting  policy is derived by means of a protograph extrinsic information transfer analysis. We show that the threshold of tailbiting SC-LDPC code ensembles can be made close to that of terminated code ensembles while avoiding the rate loss (due to termination). The analysis is complemented by Monte Carlo simulations, which confirm the viability of the approach.

Connected through hyperedge HE-1 (induced by source paper "analysis and optimization of tail biting spatially coupled protograph ldpc codes for bicm id systems"):
  with members:
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"
    - "compact qc ldpc block and sc ldpc convolutional codes for low latency communications"
    - "design and analysis of time invariant sc ldpc convolutional codes with small constraint length"
    - "wave like decoding of tail biting spatially coupled ldpc codes through iterative demapping"
    - "finite length analysis of spatially coupled regular ldpc ensembles on burst erasure channels"
    - "efficient multi dimensional mapping using qam constellations for bicm id"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"

Connected through hyperedge HE-2 (induced by source paper "outage limit approaching protograph ldpc codes for slow fading wireless communications"):
  with members:
    - "on the tanner graph cycle distribution of random ldpc random protograph based ldpc and random quasi cyclic ldpc code ensembles"
    - "construction of near capacity protograph ldpc code sequences with block error thresholds"
    - "quasi cyclic ldpc codes based on pre lifted protographs"
    - "low density graph codes for coded cooperation on slow fading relay 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)