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: "relief r cnn utilizing convolutional feature interrelationship for fast object detection deployment"
  Abstract: R-CNN style models are the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification for objects. The proposal generation phase in this paradigm is usually time consuming and not convenient to be deployed on hardwares with limited computational ability. This article shows that the high-level patterns of feature value in deep convolutional feature maps contain plenty of useful spatial information, and propose a simple manually designed approach that can extract the information for fast region proposal generation. The proposed method, dubbed Relief R-CNN($R^2$-CNN), is a new deep learning approach for object detection. By extracting positions of objects from high-level convolutional patterns, $R^2$-CNN generates region proposals and performs deep classification simultaneously based on the same forwarding CNN features, unifies the formerly separated object detection process and speeds up the whole pipeline without additional training. Empirical studies show that $R^2$-CNN could achieve the best trade-off between speed and detection performance among all the comparison algorithms.

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)