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: "small size relative p epsilon approximations for well behaved range spaces"
  Abstract: We present improved upper bounds for the size of relative (p,Epsilon)-approximation for range spaces with the following property: For any (finite) range space projected onto (that is, restricted to) a ground set of size n and for any parameter 1 <= k <= n, the number of ranges of size at most k is only nearly-linear in n and polynomial in k. Such range spaces are called "well behaved". Our bound is an improvement over the bound O(\log{(1/p)/\eps^2 p) introduced by Li etal. for the general case (where this bound has been shown to be tight in the worst case), when p << Epsilon. We also show that such small size relative (p,Epsilon)-approximations can be constructed in expected polynomial time. #R##N#Our bound also has an interesting interpretation in the context of "p-nets": As observed by Har-Peled and Sharir, p-nets are special cases of relative (p,Epsilon)-approximations. Specifically, when Epsilon is a constant smaller than 1, their analysis implies that there are p-nets of size O(\log{(1/p)}/p) that are \emph{also} relative approximations. In this context our construction significantly improves this bound for well-behaved range spaces. Despite the progress in the theory of p-nets and the existence of improved bounds corresponding to the cases that we study, these bounds do not necessarily guarantee a bounded relative error. #R##N#Lastly, we present several geometric scenarios of well-behaved range spaces, and show the resulting bound for each of these cases obtained as a consequence of our analysis. In particular, when Epsilon is a constant smaller than 1, our bound for points and axis-parallel boxes in two and three dimensions, as well as points and "fat" triangles in the plane, matches the optimal bound for p-nets.

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.CG(Computational Geometry)