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: "scalable support vector machine for semi supervised learning"
  Abstract: Owing to the prevalence of unlabeled data, semisupervised learning has been one of the most prominent machine learning paradigms, and applied successfully in many real-world applications. However, most of existing semi-supervised learning methods are neither computationally efficient nor economic in memory usage. In this paper, we present the Graph-based Semisupervised Kernel Machine (GKM), a method that leverages the generalization ability of kernel-based method with the geometrical and distributive information carried in a spectral graph induced from data for semi-supervised learning purpose. Our proposed GKM can be solved directly in the primal form using the Stochastic Gradient Descent method with the ideal convergence rate $O(\frac{1}{T})$. Besides, our formulation is suitable for a wide spectrum of important loss functions in the literature of machine learning (e.g., Hinge, smooth Hinge, Logistic, L1, and {\epsilon}-insensitive) and smoothness functions (i.e., $l_p(t) = |t|^p$ with $p\ge1$). We note that the well-known Laplacian Support Vector Machine falls into the spectrum of gS3VM corresponding to the combination of the Hinge loss and the smoothness function $l_2(.)$. We further show that the well-known Laplacian Support Vector Machine is a special case of our formulation. We validate our proposed method on several benchmark datasets to demonstrate that GKM is appropriate for the large-scale datasets since it is optimal in memory usage and yields superior classification accuracy whilst simultaneously achieving a significant computation speed-up in comparison with the state-of-the-art baselines.

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.LG(Machine Learning)