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: "on semantic word cloud representation"
  Abstract: We study the problem of computing semantic-preserving word clouds in which semantically related words are close to each other. While several heuris- tic approaches have been described in the literature, we formalize the underlying geometric algorithm problem: Word Rectangle Adjacency Contact (WRAC). In this model each word is a rectangle with fixed dimensions, and the goal is to rep- resent semantically related word pairs by contacts between their corresponding rectangles. We design and analyze efficient polynomial-time algorithms for vari- ants of the WRAC problem, show that some general variants are NP-hard, and describe several approximation algorithms. Finally, we experimentally demon- strate that our theoretically-sound algorithms outperform the early heuristics. 1 Introduction Word clouds and tag clouds are popular tools for visualizing text. The practical tool, Wordle (21) took word clouds to the next level with high quality design, graphics, style and functionality. Such word cloud visualizations provide an appealing way to sum- marize the content of a webpage, a research paper, or a political speech. Often such visualizations are used to contrast two documents; for example, word cloud visualiza- tions of the speeches given by the candidates in the 2008 US Presidential elections were used to draw sharp contrast between them in the popular media. While some of the more recent word cloud visualization tools aim to incorporate semantics in the layout, none provide any guarantees about the quality of the layout in terms of semantics. We propose a formal model of the problem, via a simple vertex- weighted and edge-weighted graph. The vertices in the graph are the words in the doc- ument, with weights corresponding to their frequency (or normalized frequency). The edges in the graph correspond to semantic relatedness, with weights corresponding to the strength of the relation. Each vertex must be drawn as a rectangle or box with fixed dimensions and with area determined by its weight. The goal is to "realize" as many edges as possible, by contacts between their corresponding rectangles; see Fig. 1.

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.DS(Data Structures and Algorithms)