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: "obtaining better image representations by combining complementary activation features of multiple convnet layers for transfer learning"
  Abstract: Convolutional Networks (ConvNets) are powerful models that learn hierarchies of visual features, which could also be used as an image representations in transfer learning. The Basic pipeline for transfer learning is to first train a ConvNet on a large dataset (source task) and then use feed-forward units activation of the trained network, at a certain layer, as a generic representation of an input image for another smaller dataset (target task). Our key contribution is to introduce a notion of better transfer learning using multiple ConvNet layer features. Idea is based on the difference of ConvNet Layer features in describing images when transfer learning. We demonstrate this idea by achieving better performance in classification against single ConvNet layer features for three standard datasets. When we combine multiple ConvNet layer features by simply concatenating them, most of the resulting features would become repetitive. We use AdaBoost.MH classifier to implicitly select distinct features from among the combined ConvNet Layer features. Our experiments intends to show this complementary nature of ConvNet layer features when doing transfer learning. We use AdaBoost.MH classifier to show that one can achieve superior results with Combined ConvNet Layer features against individual ConvNet Layer features. To emphasize the importance of careful feature selection among combined ConvNet Layer features we also train linear SVM classifier. In our experiments with AdaBoost.MH being a classifier, combined multiple ConvNet layer features always outperform Single ConvNet layer features.

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)