human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "object detection via a multi region semantic segmentation aware cnn model"):
  with members:
    - "you only look once unified real time object detection"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "deep cnn ensemble with data augmentation for object detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "object detection networks on convolutional feature maps"

Connected through hyperedge HE-2 (induced by source paper "minimally supervised feature selection for classification master s thesis university politehnica of bucharest"):
  with members:
    - "gradient boosted feature selection"
    - "labeling the features not the samples efficient video classification with minimal supervision"
    - "caffe convolutional architecture for fast feature embedding"
    - "thoughts on a recursive classifier graph a multiclass network for deep object recognition"
    - "efficient regularized regression for variable selection with l0 penalty"
    - "provable bounds for learning some deep representations"
    - "multiple decision trees"
    - "popular ensemble methods an empirical study"

Question: Please tell me which class the center node 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)