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: "a review of eo image information mining"
  Abstract: We analyze the state of the art of content-based retrieval in Earth observation image archives focusing on complete systems showing promise for operational implementation. The different paradigms at the basis of the main system families are introduced. The approaches taken are analyzed, focusing in particular on the phases after primitive feature extraction. The solutions envisaged for the issues related to feature simplification and synthesis, indexing, semantic labeling are reviewed. The methodologies for query specification and execution are analyzed.

Connected through hyperedge HE-1 (induced by source paper "efficient processing of raster and vector data"):
  with members:
    - "aggregated 2d range queries on clustered points"

Connected through hyperedge HE-2 (induced by source paper "exploiting deep features for remote sensing image retrieval a systematic investigation"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "texture characterization using shape co occurrence patterns"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "distributed mining of large scale remote sensing image archives on public computing infrastructures"
    - "cross dimensional weighting for aggregated deep convolutional features"

Connected through hyperedge HE-3 (induced by source paper "distributed mining of large scale remote sensing image archives on public computing infrastructures"):
  with members:
    - "scalable k means"

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.IR(Information Retrieval)