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 systematic comparison of deep learning architectures in an autonomous vehicle"
  Abstract: Self-driving technology is advancing rapidly, largely due to recent developments in deep learning algorithms. To date, however, there has been no systematic comparison of how different deep learning architectures perform at such tasks, or an attempt to determine a correlation between classification performance and performance in an actual vehicle. Here, we introduce the first controlled comparison of seven contemporary deep-learning architectures in an end-to-end autonomous driving task. We use a simple and affordable platform consisting of of an off-the-shelf, remotely operated vehicle, a GPU equipped computer and an indoor foam-rubber racetrack. We compare a fully-connected network, a 2-layer CNN, AlexNet, VGG-16, Inception-V3, ResNet-26, and LSTM and report the number of laps they are able to successfully complete without crashing while traversing an indoor racetrack under identical testing conditions. Based on these tests, AlexNet completed the most laps without crashing out of all networks, and ResNet-26 is the most 'efficient' architecture examined, with respect to the number of laps completed relative to the number of parameters. We also observe whether spatial, color, or temporal features - or some combination - are more important for such tasks. Finally, we show that validation loss/accuracy is not sufficiently indicative of the model's performance even when employed in a real vehicle with a simple task, emphasizing the need for greater accessibility to research platforms within the self-driving community.

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.LG(Machine Learning)