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: "a simple riemannian manifold network for image set classification"
  Abstract: In the domain of image-set based classification, a considerable advance has been made by representing original image sets as covariance matrices which typical lie in a Riemannian manifold. Specifically, it is a Symmetric Positive Definite (SPD) manifold. Traditional manifold learning methods inevitably have the property of high computational complexity or weak performance of the feature representation. In order to overcome these limitations, we propose a very simple Riemannian manifold network for image set classification. Inspired by deep learning architectures, we design a fully connected layer to generate more novel, more powerful SPD matrices. However we exploit the rectifying layer to prevent the input SPD matrices from being singular. We also introduce a non-linear learning of the proposed network with an innovative objective function. Furthermore we devise a pooling layer to further reduce the redundancy of the input SPD matrices, and the log-map layer to project the SPD manifold to the Euclidean space. For learning the connection weights between the input layer and the fully connected layer, we use Two-directional two-dimensional Principal Component Analysis ((2D)2PCA) algorithm. The proposed Riemannian manifold network (RieMNet) avoids complex computing and can be built and trained extremely easy and efficient. We have also developed a deep version of RieMNet, named as DRieMNet. The proposed RieMNet and DRieMNet are evaluated on three tasks: video-based face recognition, set-based object categorization, and set-based cell identification. Extensive experimental results show the superiority of our method over the state-of-the-art.

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)