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: "confluent drawing parallel coordinates web based interactive visual analytics of large multi dimensional data"
  Abstract: Parallel coordinates plot is one of the most popular and widely used visualization techniques for multi-dimensional data sets. Its main challenges for large-scale data sets are visual clutter and overplotting which hamper the recognition of patterns and trends in the data. In this paper, we propose a confluent drawing approach of parallel coordinates to support the web-based interactive visual analytics of large multi-dimensional data. The proposed method maps multi-dimensional data to node-link diagrams through the data binning-based clustering for each dimension. It uses density-based confluent drawing to visualize clusters and edges to reduce visual clutter and overplotting. Its rendering time is independent of the number of data items. It supports interactive visualization of large data sets without hardware acceleration in a normal web browser. Moreover, we design interactions to control the data binning process with this approach to support interactive visual analytics of large multi-dimensional data sets. Based on the proposed approach, we implement a web-based visual analytics application. The efficiency of the proposed method is examined through experiments on several data sets. The effectiveness of the proposed method is evaluated through a user study, in which two typical tasks of parallel coordinates plot are performed by participants to compare the proposed method with another parallel coordinates bundling technique. Results show that the proposed method significantly enhances the web-based interactive visual analytics of large multi-dimensional data.

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.CV(Computer Vision and Pattern Recognition)