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: "modular vehicle control for transferring semantic information to unseen weather conditions using gans"
  Abstract: End-to-end supervised learning has shown promising results for self-driving cars, particularly under conditions for which it was trained. However, it may not necessarily perform well under unseen conditions. In this paper, we demonstrate how knowledge can be transferred from one weather condition for which semantic labels and steering commands are available to a completely new set of conditions for which we have no access to labeled data. The problem is addressed by dividing the task of vehicle control into independent perception and control modules, such that changing one does not affect the other. We train the control module only on the data for the available condition and keep it fixed even under new conditions. The perception module is then used as an interface between the new weather conditions and this control model. The perception module in turn is trained using semantic labels, which we assume are already available for the same weather condition on which the control model was trained. However, obtaining them for other conditions is a tedious and error-prone process. Therefore, we propose to use a generative adversarial network (GAN)-based model to retrieve the semantic information for the new conditions in an unsupervised manner. We introduce a master-servant architecture, where the master model (semantic labels available) trains the servant model (semantic labels not available). The servant model can then be used for steering the vehicle without retraining the control module.

Connected through hyperedge HE-1 (induced by source paper "towards generalizing sensorimotor control across weather conditions"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "multimodal unsupervised image to image translation"
    - "on evaluation of embodied navigation agents"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "on offline evaluation of vision based driving models"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "latent space reinforcement learning for steering angle prediction"):
  with members:
    - "autonomous driving in reality with reinforcement learning and image translation"
    - "virtual to real reinforcement learning for autonomous driving"
    - "end to end learning for self driving cars"
    - "continuous control with deep reinforcement learning"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-3 (induced by source paper "semantic label reduction techniques for autonomous driving"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "dsnet for real time driving scene semantic segmentation"
    - "grad cam improved visual explanations for deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "towards self supervised high level sensor fusion"):
  with members:
    - "semantic label reduction techniques for autonomous driving"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-5 (induced by source paper "learning to drive from simulation without real world labels"):
  with members:
    - "video to video synthesis"
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "multimodal unsupervised image to image translation"
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "driving policy transfer via modularity and abstraction"
    - "learning to drive in a day"
    - "on offline evaluation of vision based driving models"
    - "semi convolutional operators for instance segmentation"

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)