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: "semantic driven multi camera pedestrian detection"
  Abstract: Nowadays, pedestrian detection is one of the pivotal fields in computer vision, especially when performed over video surveillance scenarios. People detection methods are highly sensitive to occlusions among pedestrians, which dramatically degrades performance in crowded scenarios. The cutback in camera prices has allowed generalizing multi-camera set-ups, which can better confront occlusions by using different points of view to disambiguate detections. In this paper we present an approach to improve the performance of these multi-camera systems and to make them independent of the considered scenario, via an automatic understanding of the scene content. This semantic information, obtained from a semantic segmentation, is used 1) to automatically generate a common Area of Interest for all cameras, instead of the usual manual definition of this area; and 2) to improve the 2D detections of each camera via an optimization technique which maximizes coherence of every detection both in all 2D views and in the 3D world, obtaining best-fitted bounding boxes and a consensus height for every pedestrian. Experimental results on five publicly available datasets show that the proposed approach, which does not require any training stage, outperforms state-of-the-art multi-camera pedestrian detectors non specifically trained for these datasets, which demonstrates the expected semantic-based robustness to different scenarios.

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)