human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "recent progress in semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "knowledge distillation for incremental learning in semantic segmentation")
  Member "deep learning and its application to medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "addressing the memory bottleneck in ai model training")
  Member "polar transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "person re identification by contour sketch under moderate clothing change")
  Member "what uncertainties do we need in bayesian deep learning for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

Question: Please tell me which class the source paper 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)