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 "features in concert discriminative feature selection meets unsupervised clustering"):
  Abstract: Feature selection is an essential problem in computer vision, important for category learning and recognition. Along with the rapid development of a wide variety of visual features and classifiers, there is a growing need for efficient feature selection and combination methods, to construct powerful classifiers for more complex and higher-level recognition tasks. We propose an algorithm that efficiently discovers sparse, compact representations of input features or classifiers, from a vast sea of candidates, with important optimality properties, low computational cost and excellent accuracy in practice. Different from boosting, we start with a discriminant linear classification formulation that encourages sparse solutions. Then we obtain an equivalent unsupervised clustering problem that jointly discovers ensembles of diverse features. They are independently valuable but even more powerful when united in a cluster of classifiers. We evaluate our method on the task of large-scale recognition in video and show that it significantly outperforms classical selection approaches, such as AdaBoost and greedy forward-backward selection, and powerful classifiers such as SVMs, in speed of training and performance, especially in the case of limited training data.

Includes members:
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "minimally supervised feature selection for classification master s thesis university politehnica of bucharest")
  Member "multiple decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "minimally supervised feature selection for classification master s thesis university politehnica of bucharest")
  Member "popular ensemble methods an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "batchensemble an alternative approach to efficient ensemble and lifelong learning")

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)