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: "exploration of awgnc and bsc pseudocodeword redundancy"
  Abstract: The AWGNC, BSC, and max-fractional pseu- docodeword redundancy ½(C) of a code C is defined as the smallest number of rows in a parity-check matrix such that the corresponding minimum pseudoweight is equal to the minimum Hamming distance of C. This paper provides new results on the AWGNC, BSC, and max-fractional pseudocodeword redundancies of codes. The pseudocodeword redundancies for all codes of small length (at most 9) are computed. Also, comprehensive results are provided on the cases of cyclic codes of length at most 250 for which the eigenvalue bound of Vontobel and Koetter is sharp. I. I NTRODUCTION Pseudocodewords play a significant role in the finite- length analysis of binary linear low-density parity-check (LDPC) codes under linear-programming (LP) or message- passing (MP) decoding (see e.g. (1), (2)). The concept of pseudoweight of a pseudocodeword was introduced in (3) as an analog to the pertinent parameter in the maximum likelihood (ML) decoding scenario, i.e. the signal Euclidean distance in the case of the additive white Gaussian noise channel (AWGNC), or the Hamming distance in the case of the binary symmetric channel (BSC). Accordingly, for a binary linear code C and a parity-check matrix H of C, the (AWGNC or BSC) minimum pseudoweight w min (H) may be considered as a first-order measure of decoder error- correcting performance for LP or MP decoding. Note that w min (H) may be different for different matrices H: adding redundant rows to H introduces additional constraints on the so-called fundamental cone and may thus increase the minimum pseudoweight. Another closely related measure is the max-fractional weight (pseudoweight). It serves as a lower bound on both AWGNC and BSC pseudoweights. The AWGNC (or BSC) pseudocodeword redundancy ρAWGNC(C) (or ρBSC(C), respectively) of a code C is defined as the minimum number of rows in a parity-check matrix H such that the corresponding minimum pseudoweight w min (H) is as large as its minimum Hamming distance d. It is set to infinity if there is no such matrix. We sometimes simply write ρ(C), when the type of the channel is clear from the context.

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)