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 "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation"):
  Abstract: The fusion of multiple sensor modalities, especially through deep learning architectures, has been an active area of study. However, an under-explored aspect of such work is whether the methods can be robust to degradations across their input modalities, especially when they must generalize to degradations not seen during training. In this work, we propose an uncertainty-aware fusion scheme to effectively fuse inputs that might suffer from a range of known and unknown degradations. Specifically, we analyze a number of uncertainty measures, each of which captures a different aspect of uncertainty, and we propose a novel way to fuse degraded inputs by scaling modality-specific output softmax probabilities. We additionally propose a novel data-dependent spatial temperature scaling method to complement these existing uncertainty measures. Finally, we integrate the uncertainty-scaled output from each modality using a probabilistic noisy-or fusion method. In a photo-realistic simulation environment (AirSim), we show that our method achieves significantly better results on a semantic segmentation task, compared to state-of-art fusion architectures, on a range of degradations (e.g. fog, snow, frost, and various other types of noise), some of which are unknown during training. We specifically improve upon the state-of-art[1] by 28% in mean IoU on various degradations. [1] Abhinav Valada, Rohit Mohan, and Wolfram Burgard. Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. In: arXiv e-prints, arXiv:1808.03833 (Aug. 2018), arXiv:1808.03833. arXiv: 1808.03833 [cs.CV].

Includes members:
  Member "self supervised model adaptation for multimodal semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "deep surface normal estimation with hierarchical rgb d fusion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "segmenting unseen industrial components in a heavy clutter using rgb d fusion and synthetic data")
  Member "on single source robustness in deep fusion models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spocc scalable possibilistic classifier combination toward robust aggregation of classifiers")
  Member "benchmarking neural network robustness to common corruptions and perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "the fishyscapes benchmark measuring blind spots in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learnable bernoulli dropout for bayesian deep learning")
  Member "benchmarking robustness in object detection autonomous driving when winter is coming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "proposal learning for semi supervised object detection")
  Member "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images")
  Member "evaluating bayesian deep learning methods for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scalable uncertainty for computer vision with functional variational inference")

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.CV(Computer Vision and Pattern Recognition)