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 "validating hyperspectral image segmentation"):
  Abstract: Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, and hence, automated approaches for precise segmentation of such imagery are being rapidly developed. In this letter, we share our observations on the strategy for validating hyperspectral image segmentation algorithms currently followed in the literature, and show that it can lead to overoptimistic experimental insights. We introduce a new routine for generating segmentation benchmarks and use it to elaborate ready-to-use hyperspectral training–test data partitions. They can be utilized for fair validation of new and existing algorithms without any training–test data leakage.

Includes members:
  Member "going deeper with contextual cnn for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for classification of hyperspectral data a comparative review")
  Member "bass net band adaptive spectral spatial feature learning neural network for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a cnn with multi scale convolution for hyperspectral image classification using target pixel orientation scheme")
  Member "cascaded recurrent neural networks for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bs nets an end to end framework for band selection of hyperspectral image")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)