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: "lates latent space distillation for teacher student driving policy learning"
  Abstract: We describe a policy learning approach to map visual inputs to driving controls that leverages side information on semantics and affordances of objects in the scene from a secondary teacher model. While the teacher receives semantic segmentation and stop "intention" values as inputs and produces an estimate of the driving controls, the primary student model only receives images as inputs, and attempts to imitate the controls while being biased towards the latent representation of the teacher model. The latent representation encodes task-relevant information in the inputs of the teacher model, which are semantic segmentation of the image, and intention values for driving controls in the presence of objects in the scene such as vehicles, pedestrians and traffic lights. Our student model does not attempt to infer semantic segmentation or intention values from its inputs, nor to mimic the output behavior of the teacher. It instead attempts to capture the representation of the teacher inputs that are relevant for driving. Our training does not require laborious annotations such as maps or objects in three dimensions; even the teacher model just requires two-dimensional segmentation and intention values. Moreover, our model runs in real time of 59 FPS. We test our approach on recent simulated and real-world driving datasets, and introduce a more challenging but realistic evaluation protocol that considers a run that reaches the destination successful only if it does not violate common traffic rules.

Connected through hyperedge HE-1 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios"):
  with members:
    - "periphery fovea multi resolution driving model guided by human attention"
    - "salient object detection in the deep learning era an in depth survey"
    - "unsupervised traffic accident detection in first person videos"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "yolov3 an incremental improvement"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "latent space autoregression for novelty detection"

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)