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: "k means approach to the karhunen lo eve transform"
  Abstract: Abstract—We present a simultaneous generalization of thewell-known Karhunen-Lo´eve (PCA) and k-means algorithms.The basic idea lies in approximating the data with k afﬁnesubspaces of a given dimension n. In the case n = 0 we obtainthe classical k-means, while for k = 1 we obtain PCA algorithm.We show that for some data exploration problems this methodgives better result then either of the classical approaches.Index Terms—Karhunen-Loeve Transform, PCA, k-Means,´optimization, compression, data compression, image compression. I. I NTRODUCTION O UR general problem concerns splitting of a given data-set W into clusters with respect to their intrinsic di-mensionality. The motivation to create such an algorithm is adesire to extract parts of data which can be easily describedby a smaller number of parameters. More precisely, we wantto ﬁnd afﬁne spaces S 1 ;:::;S k such that every element ofW belongs (with certain maximal error) to one of the spacesS 1 ;:::;S k .To explain it graphically, let us consider following example.Figure 1(a) represents three lines in the plane, while Figure1(b) a circle and an orthogonal line in the space. Our goal isto construct an algorithm that will split them into three linesand into a line and a circle.

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.IT(Information Theory)