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: "joint detection decoding algorithms for nonbinary ldpc codes over isi channels"
  Abstract: This paper is concerned with the application of nonbinary low-density parity-check (NB-LDPC) codes to binary input inter-symbol interference (ISI) channels. Two low-complexity joint detection/decoding algorithms are proposed. One is referred to as max-log-MAP/X-EMS algorithm, which is implemented by exchanging soft messages between the max-log-MAP detector and the extended min-sum (EMS) decoder. The max-log-MAP/X-EMS algorithm is applicable to general NB-LDPC codes. The other one, referred to as Viterbi/GMLGD algorithm, is designed in particular for majority-logic decodable NB-LDPC codes. The Viterbi/GMLGD algorithm works in an iterative manner by exchanging hard-decisions between the Viterbi detector and the generalized majority-logic decoder(GMLGD). As a by-product, a variant of the original EMS algorithm is proposed, which is referred to as \mu-EMS algorithm. In the \mu-EMS algorithm, the messages are truncated according to an adaptive threshold, resulting in a more efficient algorithm. Simulations results show that the max-log-MAP/X-EMS algorithm performs as well as the traditional iterative detection/decoding algorithm based on the BCJR algorithm and the QSPA, but with lower complexity. The complexity can be further reduced for majority-logic decodable NB-LDPC codes by executing the Viterbi/GMLGD algorithm with a performance degradation within one dB. Simulation results also confirm that the \mu-EMS algorithm requires lower computational loads than the EMS algorithm with a fixed threshold. These algorithms provide good candidates for trade-offs between performance and complexity.

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)