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: "safedrive enhancing lane appearance for autonomous and assisted driving under limited visibility"
  Abstract: Autonomous detection of lane markers improves road safety, and purely visual tracking is desirable for widespread vehicle compatibility and reducing sensor intrusion, cost, and energy consumption. However, visual approaches are often ineffective because of a number of factors; e.g., occlusion, poor weather conditions, and paint wear-off. We present an approach to enhance lane marker appearance for assisted and autonomous driving, particularly under poor visibility. Our method, named SafeDrive, attempts to improve visual lane detection approaches in drastically degraded visual conditions. SafeDrive finds lane markers in alternate imagery of the road at the vehicle's location and reconstructs a sparse 3D model of the surroundings. By estimating the geometric relationship between this 3D model and the current view, the lane markers are projected onto the visual scene; any lane detection algorithm can be subsequently used to detect lanes in the resulting image. SafeDrive does not require additional sensors other than vision and location data. We demonstrate the effectiveness of our approach on a number of test cases obtained from actual driving data recorded in urban settings.

Connected through hyperedge HE-1 (induced by source paper "mixed signal charge domain acceleration of deep neural networks through interleaved bit partitioned arithmetic"):
  with members:
    - "development of a sensory neural network for medical diagnosing"
    - "yolov3 an incremental improvement"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "low latency neural speech translation"
    - "recogym a reinforcement learning environment for the problem of product recommendation in online advertising"
    - "caterpillar coarse grain reconfigurable architecture for accelerating the training of deep neural networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

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)