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: "parabolic approximation line search an efficient and effective line search approach for dnns"
  Abstract: This work shows that line searches performed only over the loss function defined by the current batch can successfully compete with common optimization methods on state-of-the-art architectures in wall clock time. In other words, our approach is performing competitively, despite for the most part ignoring the noise originating from batch sampling of the loss function. Furthermore, we empirically show that local minima on lines in direction of the negative gradient can be estimated almost perfectly by a parabolic approximation. This suggests that the loss function is at least locally convex, which mitigates the common perception of a highly non convex loss landscape. Our approach combines well-known methods such as parabolic approximation, line search and conjugate gradient, to perform an efficient line search. To evaluate general performance as well as the hyper parameter sensitivity of our optimizer, we performed multiple comprehensive hyperparameter grid searches on several datasets and architectures. In addition, we provide a convergence prove on a simplified scenario. PyTorch and Tensorflow implementations are provided at https://github.com/cogsys-tuebingen/PAL.

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)