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: "virtual to real adaptation of pedestrian detectors for smart cities"
  Abstract: Pedestrian detection through computer vision is a building block for a multitude of applications in the context of smart cities, such as surveillance of sensitive areas, personal safety, monitoring, and control of pedestrian flow, to mention only a few. Recently, there was an increasing interest in deep learning architectures for performing such a task. One of the critical objectives of these algorithms is to generalize the knowledge gained during the training phase to new scenarios having various characteristics, and a suitably labeled dataset is fundamental to achieve this goal. The main problem is that manually annotating a dataset usually requires a lot of human effort, and it is a time-consuming operation. For this reason, in this work, we introduced ViPeD - Virtual Pedestrian Dataset, a new synthetically generated set of images collected from a realistic 3D video game where the labels can be automatically generated exploiting 2D pedestrian positions extracted from the graphics engine. We used this new synthetic dataset training a state-of-the-art computationally-efficient Convolutional Neural Network (CNN) that is ready to be installed in smart low-power devices, like smart cameras. We addressed the problem of the domain-adaptation from the virtual world to the real one by fine-tuning the CNN using the synthetic data and also exploiting a mixed-batch supervised training approach. Extensive experimentation carried out on different real-world datasets shows very competitive results compared to other methods presented in the literature in which the algorithms are trained using real-world data.

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)