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 analysis of 3 dimensional turbo codes"
  Abstract: In this work, we consider the minimum distance properties and convergence thresholds of 3-dimensional turbo codes (3D-TCs), recently introduced by Berrou et al.. Here, we consider binary 3D-TCs while the original work of Berrou et al. considered double-binary codes. In the first part of the paper, the minimum distance properties are analyzed from an ensemble perspective, both in the finite-length regime and in the asymptotic case of large block lengths. In particular, we analyze the asymptotic weight distribution of 3D-TCs and show numerically that their typical minimum distance dmin may, depending on the specific parameters, asymptotically grow linearly with the block length, i.e., the 3D-TC ensemble is asymptotically good for some parameters. In the second part of the paper, we derive some useful upper bounds on the dmin when using quadratic permutation polynomial (QPP) interleavers with a quadratic inverse. Furthermore, we give examples of interleaver lengths where an upper bound appears to be tight. The best codes (in terms of estimated dmin) obtained by randomly searching for good pairs of QPPs for use in the 3D-TC are compared to a probabilistic lower bound on the dmin when selecting codes from the 3D-TC ensemble uniformly at random. This comparison shows that the use of designed QPP interleavers can improve the dmin significantly. For instance, we have found a (6144,2040) 3D-TC with an estimated dmin of 147, while the probabilistic lower bound is 69. Higher rates are obtained by puncturing nonsystematic bits, and optimized periodic puncturing patterns for rates 1/2, 2/3, and 4/5 are found by computer search. Finally, we give iterative decoding thresholds, computed from an extrinsic information transfer chart analysis, and present simulation results on the additive white Gaussian noise channel to compare the error rate performance to that of conventional turbo codes.

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)