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 "minimally supervised feature selection for classification master s thesis university politehnica of bucharest"):
  Abstract: In the context of the highly increasing number of features that are available nowadays we design a robust and fast method for feature selection. The method tries to select the most representative features that are independent from each other, but are strong together. We propose an algorithm that requires very limited labeled data (as few as one labeled frame per class) and can accommodate as many unlabeled samples. We also present here the supervised approach from which we started. We compare our two formulations with established methods like AdaBoost, SVM, Lasso, Elastic Net and FoBa and show that our method is much faster and it has constant training time. Moreover, the unsupervised approach outperforms all the methods with which we compared and the difference might be quite prominent. The supervised approach is in most cases better than the other methods, especially when the number of training shots is very limited. All that the algorithm needs is to choose from a pool of positively correlated features. The methods are evaluated on the Youtube-Objects dataset of videos and on MNIST digits dataset, while at training time we also used features obtained on CIFAR10 dataset and others pre-trained on ImageNet dataset. Thereby, we also proved that transfer learning is useful, even though the datasets differ very much: from low-resolution centered images from 10 classes, to high-resolution images with objects from 1000 classes occurring in different regions of the images or to very difficult videos with very high intraclass variance. 7

Includes members:
  Member "gradient boosted feature selection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards federated learning robustness analytics to data heterogeneity")
  Member "labeling the features not the samples efficient video classification with minimal supervision":
    also connected through other hyperedges:
      (none)
  Member "features in concert discriminative feature selection meets unsupervised clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection via a multi region semantic segmentation aware cnn model")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "thoughts on a recursive classifier graph a multiclass network for deep object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "features in concert discriminative feature selection meets unsupervised clustering")
  Member "efficient regularized regression for variable selection with l0 penalty":
    also connected through other hyperedges:
      (none)
  Member "provable bounds for learning some deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "span recovery for deep neural networks with applications to input obfuscation")
  Member "multiple decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "features in concert discriminative feature selection meets unsupervised clustering")

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)