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 "transfer learning for segmenting dimensionally reduced hyperspectral images"):
  Abstract: Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis. However, training large-capacity learners to segment such imagery requires representative training sets. Acquiring such data is human-dependent and time-consuming, especially in Earth observation scenarios, where the hyperspectral data transfer is very costly and time-constrained. In this letter, we show how to effectively deal with a limited number and size of available hyperspectral ground-truth sets, and apply transfer learning for building deep feature extractors. Also, we exploit spectral dimensionality reduction to make our technique applicable over hyperspectral data acquired using different sensors, which may capture different numbers of hyperspectral bands. The experiments, performed over several benchmarks and backed up with statistical tests, indicated that our approach allows us to effectively train well-generalizing deep convolutional neural nets even using significantly reduced data.

Includes members:
  Member "validating hyperspectral image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "segmenting hyperspectral images using spectral spatial convolutional neural networks with training time data augmentation")
  Member "is pretraining necessary for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "me r cnn multi expert r cnn for object detection")
  Member "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "feature extraction for hyperspectral imagery the evolution from shallow to deep")
  Member "band selection from hyperspectral images using attention based convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "bass net band adaptive spectral spatial feature learning neural network for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a cnn with multi scale convolution for hyperspectral image classification using target pixel orientation scheme")

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)