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: "self driving car steering angle prediction based on image recognition"
  Abstract: Self-driving vehicles have expanded dramatically over the last few years. Udacity has release a dataset containing, among other data, a set of images with the steering angle captured during driving. The Udacity challenge aimed to predict steering angle based on only the provided images. We explore two different models to perform high quality prediction of steering angles based on images using different deep learning techniques including Transfer Learning, 3D CNN, LSTM and ResNet. If the Udacity challenge was still ongoing, both of our models would have placed in the top ten of all entries.

Connected through hyperedge HE-1 (induced by source paper "controlling steering angle for cooperative self driving vehicles utilizing cnn and lstm based deep networks"):
  with members:
    - "analysis of distributed congestion control in cellular vehicle to everything networks"
    - "variational end to end navigation and localization"
    - "vision based navigation of autonomous vehicle in roadway environments with unexpected hazards"
    - "multiple access in cellular v2x performance analysis in highly congested vehicular networks"
    - "lstm a search space odyssey"
    - "end to end deep learning for steering autonomous vehicles considering temporal dependencies"
    - "end to end learning for self driving cars"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-2 (induced by source paper "aggregated sparse attention for steering angle prediction"):
  with members:
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "learning a driving simulator"
    - "end to end learning for self driving cars"
    - "shallow and deep convolutional networks for saliency prediction"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "show attend and tell neural image caption generation with visual attention"
    - "action recognition using visual attention"

Connected through hyperedge HE-3 (induced by source paper "cross domain deep face matching for real banking security systems"):
  with members:
    - "cross domain face verification matching id document and self portrait photographs"
    - "facenet a unified embedding for face recognition and clustering"

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)