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 object detection"
  Abstract: The state-of-the-art object detection pipeline needs a set of object location hypotheses followed by a deep CNN classifier. Previous research on this paradigm, usually extracts features from the same image for the two steps separately, which is time consuming and is hard to optimize. This work shows that the high-level patterns of feature values in deep convolutional feature map contain plenty of useful spatial information and proposes a new deep learning approach to object detection, namely Relief R-CNN ($R^2$-CNN). By extracting positions of objects from these high-level patterns, $R^2$-CNN generates region proposals and performs deep classification simultaneously using the same forward CNN features, unifying the formerly separated object detection process. In this way, $R^2$-CNN does not involve additional information extraction process for region proposal generation, considerably reducing the total computation costs. In addition, a recursive fine-tune technique is also developed for refining coarse proposals. Empirical results showed that our $R^2$-CNN had a very high speed and a reasonable detection rate even in the presence of limit on the proposal number, indicating that the region proposals generated by our $R^2$-CNN are in excellent quality.

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)