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: "toward automatic comparison of visualization techniques application to graph visualization"
  Abstract: Many end-user evaluations of data visualization techniques have been run during the last decades. Their results are cornerstones to build efficient visualization systems. However, designing an evaluation is always complex and time-consuming and may end in a lack of statistical evidence. The raising of modern efficient computer vision techniques may help visualization researchers to adjust their evaluation hypothesis and thus reduces the risk of failure. In this paper, we present a methodology that uses such computer vision techniques to automatically compare the efficiency of several visualization techniques. The basis of our methodology is to generate a set of images for each compared visualization technique from a common dataset and to train machine learning models (one for each set and visualization technique) to solve a given task. Our assumption is that the performance of each model allows to compare the efficiencies of the corresponding visualization techniques; as current machine learning models are not capable enough to reflect human capabilities, including their imperfections, such results should be interpreted with caution. However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations should help researchers to perform a more exhaustive study of the design space and thus should improve the final user evaluation by providing better test cases. To show that our methodology can reproduce, up to a certain level, results of user evaluations, we applied it to compare two mainstream graph visualization techniques: node-link (NL) and adjacency-matrix (MD) diagrams. We partially reproduced a user evaluation from Ghoniem et al. using two well-known deep convolutional neural networks as machine learning-based systems. Our results show up that Ghoniem et al. results can be reproduced automatically at a larger scale with our system.

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.AI(Artificial Intelligence)