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 "safe robot navigation via multi modal anomaly detection"):
  Abstract: Navigation in natural outdoor environments requires a robust and reliable traversability classification method to handle the plethora of situations a robot can encounter. Binary classification algorithms perform well in their native domain but tend to provide overconfident predictions when presented with out-of-distribution samples, which can lead to catastrophic failure when navigating unknown environments. We propose to overcome this issue by using anomaly detection on multi-modal images for traversability classification, which is easily scalable by training in a self-supervised fashion from robot experience. In this work, we evaluate multiple anomaly detection methods with a combination of uni- and multi-modal images in their performance on data from different environmental conditions. Our results show that an approach using a feature extractor and normalizing flow with an input of RGB, depth and surface normals performs best. It achieves over 95% area under the ROC curve and is robust to out-of-distribution samples.

Includes members:
  Member "the fishyscapes benchmark measuring blind spots in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learnable bernoulli dropout for bayesian deep learning")
  Member "anomaly detection using deep learning based image completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation")
  Member "gonet a semi supervised deep learning approach for traversability estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep visual mpc policy learning for navigation")
  Member "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation")
  Member "unsupervised anomaly detection with generative adversarial networks to guide marker discovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "voxblox incremental 3d euclidean signed distance fields for on board mav planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "density estimation using real nvp":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "find your own way weakly supervised segmentation of path proposals for urban autonomy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning a directional soft lane affordance model for road scenes using self supervision")

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)