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: "deep stochastic radar models"
  Abstract: Accurate simulation and validation of advanced driver assistance systems requires accurate sensor models. Modeling automotive radar is complicated by effects such as multipath reflections, interference, reflective surfaces, discrete cells, and attenuation. Detailed radar simulations based on physical principles exist but are computationally intractable for realistic automotive scenes. This paper describes a methodology for the construction of stochastic automotive radar models based on deep learning with adversarial loss connected to real-world data. The resulting model exhibits fundamental radar effects while remaining real-time capable.

Connected through hyperedge HE-1 (induced by source paper "adaptive stress testing for autonomous vehicles"):
  with members:
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "on a formal model of safe and scalable self driving cars"
    - "evaluation of automated vehicles in the frontal cut in scenario an enhanced approach using piecewise mixture models"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-2 (induced by source paper "deep spatially coherent inverse sensor models with uncertainty incorporation using the evidential framework"):
  with members:
    - "lightweight probabilistic deep networks"
    - "probably unknown deep inverse sensor modelling in radar"
    - "deep network uncertainty maps for indoor navigation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep spatially coherent occupancy maps based on radar measurements"):
  with members:
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "unsupervised representation learning with deep convolutional generative adversarial 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.RO(Robotics)