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 "distributed mining of large scale remote sensing image archives on public computing infrastructures"):
  Abstract: Earth Observation (EO) mining aims at supporting efficient access and exploration of petabyte-scale space- and airborne remote sensing archives that are currently expanding at rates of terabytes per day. A significant challenge is performing the analysis required by envisaged applications --- like for instance process mapping for environmental risk management --- in reasonable time. In this work, we address the problem of content-based image retrieval via example-based queries from EO data archives. In particular, we focus on the analysis of polarimetric SAR data, for which target decomposition theorems have proved fundamental in discovering patterns in data and characterize the ground scattering properties. To this end, we propose an interactive region-oriented content-based image mining system in which 1) unsupervised ingestion processes are distributed onto virtual machines in elastic, on-demand computing infrastructures 2) archive-scale content hierarchical indexing is implemented in terms of a "big data" analytics cluster-computing framework 3) query processing amounts to traversing the generated binary tree index, computing distances that correspond to descriptor-based similarity measures between image groups and a query image tile. We describe in depth both the strategies and the actual implementations for the ingestion and indexing components, and verify the approach by experiments carried out on the NASA/JPL UAVSAR full polarimetric data archive. We report the results of the tests performed on computer clusters by using a public Infrastructure-as-a-Service and evaluating the impact of cluster configuration on system performance. Results are promising for data mapping and information retrieval applications.

Includes members:
  Member "a review of eo image information mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient processing of raster and vector data")
  Member "scalable k means":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")

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.DC(Distributed, Parallel, and Cluster Computing)