human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "curse of dimensionality in the application of pivot based indexes to the similarity search problem"):
  Abstract: In this work we study the validity of the so-called curse of dimensionality for indexing of databases for similarity search. We perform an asymptotic analysis, with a test model based on a sequence of metric spaces $(\Omega_d)$ from which we pick datasets $X_d$ in an i.i.d. fashion. We call the subscript $d$ the dimension of the space $\Omega_d$ (e.g. for $\mathbb{R}^d$ the dimension is just the usual one) and we allow the size of the dataset $n=n_d$ to be such that $d$ is superlogarithmic but subpolynomial in $n$. #R##N#We study the asymptotic performance of pivot-based indexing schemes where the number of pivots is $o(n/d)$. We pick the relatively simple cost model of similarity search where we count each distance calculation as a single computation and disregard the rest. #R##N#We demonstrate that if the spaces $\Omega_d$ exhibit the (fairly common) concentration of measure phenomenon the performance of similarity search using such indexes is asymptotically linear in $n$. That is for large enough $d$ the difference between using such an index and performing a search without an index at all is negligeable. Thus we confirm the curse of dimensionality in this setting.

Includes members:
  Member "intrinsic dimension of a dataset what properties does one expect":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intrinsic dimension of concept lattices")
  Member "on the geometry of similarity search dimensionality curse and concentration of measure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal bayes consistency in metric spaces")

Question: Please tell me which class the source paper 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)