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: "randomized lattice decoding"
  Abstract: Sphere decoding achieves maximum-likelihood (ML) performance at the cost of exponential complexity; lattice reduction-aided successive interference cancelation (SIC) significantly reduces the decoding complexity, but exhibits a widening gap to ML performance as the dimension increases. To bridge the gap between them, this paper presents randomized lattice decoding based on Klein's sampling technique, which is a randomized version of Babai's nearest plane algorithm (i.e., SIC). To find the closest lattice point, Klein's algorithm is used to sample some lattice points and the closest among those samples is chosen. Lattice reduction increases the probability of finding the closest lattice point, and only needs to be run once during pre-processing. Further, the sampling can operate very efficiently in parallel. The technical contribution of this paper is two-fold: we analyze and optimize the performance of randomized lattice decoding resulting in reduced decoding complexity, and propose a very efficient implementation of random rounding. Simulation results demonstrate near-ML performance achieved by a moderate number of samples, when the dimension is not too large. Compared to existing decoders, a salient feature of randomized lattice decoding is that it will sample a closer lattice point with higher probability. A byproduct is that boundary errors for finite constellations can be partially compensated if we discard the samples falling outside of the constellation.

Connected through hyperedge HE-1 (induced by source paper "variants of the lll algorithm in digital communications complexity analysis and fixed complexity implementation"):
  with members:
    - "on the proximity factors of lattice reduction aided decoding"
    - "augmented lattice reduction for mimo decoding"
    - "lr aided mmse lattice decoding is dmt optimal for all approximately universal codes"
    - "lll reduction achieves the receive diversity in mimo decoding"

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)