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: "generative one shot learning gol a semi parametric approach to one shot learning in autonomous vision"
  Abstract: Highly Autonomous Driving (HAD) systems rely on deep neural networks for the visual perception of the driving environment. Such networks are train on large manually annotated databases. In this work, a semi-parametric approach to one-shot learning is proposed, with the aim of bypassing the manual annotation step required for training perceptions systems used in autonomous driving. The proposed generative framework, coined Generative One-Shot Learning (GOL), takes as input single one-shot objects, or generic patterns, and a small set of so-called regularization samples used to drive the generative process. New synthetic data is generated as Pareto optimal solutions from one-shot objects using a set of generalization functions built into a generalization generator. GOL has been evaluated on environment perception challenges encountered in autonomous vision.

Connected through hyperedge HE-1 (induced by source paper "effortless deep training for traffic sign detection using templates and arbitrary natural images"):
  with members:
    - "self driving cars a survey"
    - "self paced deep learning for weakly supervised object detection"
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "few shot object detection via feature reweighting"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "lstd a low shot transfer detector for object detection"
    - "deep learning based large scale automatic satellite crosswalk classification"
    - "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"

Connected through hyperedge HE-2 (induced by source paper "aiba an ai model for behavior arbitration in autonomous driving"):
  with members:
    - "deep grid net dgn a deep learning system for real time driving context understanding"
    - "gridsim a vehicle kinematics engine for deep neuroevolutionary control in autonomous driving"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "on a formal model of safe and scalable self driving cars"
    - "end to end learning for self driving cars"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

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)