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: "the fishyscapes benchmark measuring blind spots in semantic segmentation"
  Abstract: Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for uncertainty estimation in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates and covers the detection of both out-of-distribution objects and misclassifications. We adapt state-of-the-art methods to recent semantic segmentation models and compare approaches based on softmax confidence, Bayesian learning, and embedding density. A thorough evaluation of these methods reveals a clear gap to their alleged capabilities. Our results show that failure detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "learnable bernoulli dropout for bayesian deep learning"):
  with members:
    - "adaptive activity monitoring with uncertainty quantification in switching gaussian process models"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "vae with a vampprior"
    - "understanding deep learning requires rethinking generalization"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-2 (induced by source paper "safe robot navigation via multi modal anomaly detection"):
  with members:
    - "anomaly detection using deep learning based image completion"
    - "gonet a semi supervised deep learning approach for traversability estimation"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "density estimation using real nvp"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "what can robotics research learn from computer vision research"):
  with members:
    - "a mask rcnn baseline for probabilistic object detection"
    - "do imagenet classifiers generalize to imagenet"
    - "the limits and potentials of deep learning for robotics"
    - "yolov3 an incremental improvement"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "towards open world recognition"
    - "orb slam a versatile and accurate monocular slam system"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "simultaneous semantic segmentation and outlier detection in presence of domain shift"):
  with members:
    - "efficient ladder style densenets for semantic segmentation of large images"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "taskonomy disentangling task transfer learning"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection with outlier exposure"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (induced by source paper "learning densities in feature space for reliable segmentation of indoor scenes"):
  with members:
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "fusion volumetric object level slam"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

Connected through hyperedge HE-6 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "on single source robustness in deep fusion models"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "robust deep multi modal learning based on gated information fusion network"

Connected through hyperedge HE-7 (induced by source paper "active learning for uav based semantic mapping"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "modular sensor fusion for semantic segmentation"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "cost effective active learning for deep image classification"
    - "distance based confidence score for neural network classifiers"

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.CV(Computer Vision and Pattern Recognition)