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: "probably unknown deep inverse sensor modelling in radar"
  Abstract: Radar presents a promising alternative to lidar and vision in autonomous vehicle applications, being able to detect objects at long range under a variety of weather conditions. However, distinguishing between occupied and free space from a raw radar scan is notoriously difficult. We consider the challenge of learning an Inverse Sensor Model (ISM) mapping a raw radar observation to occupancy probabilities in a discretised space. We frame this problem as a segmentation task, utilising a deep neural network that is able to learn an inherently probabilistic ISM from raw sensor data considers scene context. In doing so our approach explicitly accounts for the heteroscedastic aleatoric uncertainty for radar that arises due to complex interactions between occlusion and sensor noise. Our network is trained using only partial occupancy labels generated from lidar and able to successfully distinguish between occupied and free space. We evaluate our approach on five hours of data recorded in a dynamic urban environment and show that it significantly outperforms classical constant false-alarm rate (CFAR) filtering approaches in light of challenging noise artefacts whilst identifying space that is inherently uncertain because of occlusion.

Connected through hyperedge HE-1 (induced by source paper "road scene understanding by occupancy grid learning from sparse radar clusters using semantic segmentation"):
  with members:
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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"
    - "deep network uncertainty maps for indoor navigation"
    - "deep stochastic radar models"
    - "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 "self supervised occupancy grid learning from sparse radar for autonomous driving"):
  with members:
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image 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.RO(Robotics)