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 "bigearthnet dataset with a new class nomenclature for remote sensing image understanding"):
  Abstract: This paper presents BigEarthNet that is a large-scale Sentinel-2 multispectral image dataset with a new class nomenclature to advance deep learning (DL) studies in remote sensing (RS). BigEarthNet is made up of 590,326 image patches annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 based on its most thematic detailed Level-3 class nomenclature. Initial research demonstrates that some CLC classes are challenging to be accurately described by considering only Sentinel-2 images. To increase the effectiveness of BigEarthNet, in this paper we introduce an alternative class-nomenclature to allow DL models for better learning and describing the complex spatial and spectral information content of the Sentinel-2 images. This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of Sentinel-2 images in a new nomenclature of 19 classes. Then, the new class-nomenclature of BigEarthNet is used within state-of-the-art DL models in the context of multi-label classification. Results show that the models trained from scratch on BigEarthNet outperform those pre-trained on ImageNet, especially in relation to some complex classes including agriculture, other vegetated and natural environments. All DL models are made publicly available at this http URL, offering an important resource to guide future progress on RS image analysis.

Includes members:
  Member "a novel multi attention driven system for multi label remote sensing image classification":
    also connected through other hyperedges:
      (none)
  Member "bigearthnet a large scale benchmark archive for remote sensing image understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks")
  Member "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bigearthnet deep learning models with a new class nomenclature for remote sensing image understanding")
  Member "building instance classification using street view images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bigearthnet deep learning models with a new class nomenclature for remote sensing image understanding")
  Member "towards multi class object detection in unconstrained remote sensing imagery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "arbitrary oriented object detection with circular smooth label")
  Member "aid a benchmark data set for performance evaluation of aerial scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis")
  Member "beyond rgb very high resolution urban remote sensing with multimodal deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")

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.CV(Computer Vision and Pattern Recognition)