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: "pinball loss minimization for one bit compressive sensing"
  Abstract: The one-bit quantization can be implemented by one single comparator, which operates at low power and a high rate. Hence one-bit compressive sensing (\emph{1bit-CS}) becomes very attractive in signal processing. When the measurements are corrupted by noise during signal acquisition and transmission, 1bit-CS is usually modeled as minimizing a loss function with a sparsity constraint. The existing loss functions include the hinge loss and the linear loss. Though 1bit-CS can be regarded as a binary classification problem because a one-bit measurement only provides the sign information, the choice of the hinge loss over the linear loss in binary classification is not true for 1bit-CS. Many experiments show that the linear loss performs better than the hinge loss for 1bit-CS. Motivated by this observation, we consider the pinball loss, which provides a bridge between the hinge loss and the linear loss. Using this bridge, two 1bit-CS models and two corresponding algorithms are proposed. Pinball loss iterative hard thresholding improves the performance of the binary iterative hard theresholding proposed in [6] and is suitable for the case when the sparsity of the true signal is given. Elastic-net pinball support vector machine generalizes the passive model proposed in [11] and is suitable for the case when the sparsity of the true signal is not given. A fast dual coordinate ascent algorithm is proposed to solve the elastic-net pinball support vector machine problem, and its convergence is proved. The numerical experiments demonstrate that the pinball loss, as a trade-off between the hinge loss and the linear loss, improves the existing 1bit-CS models with better performances.

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)