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: "evaluating bayesian deep learning methods for semantic segmentation"
  Abstract: Deep learning has been revolutionary for computer vision and semantic segmentation in particular, with Bayesian Deep Learning (BDL) used to obtain uncertainty maps from deep models when predicting semantic classes. This information is critical when using semantic segmentation for autonomous driving for example. Standard semantic segmentation systems have well-established evaluation metrics. However, with BDL's rising popularity in computer vision we require new metrics to evaluate whether a BDL method produces better uncertainty estimates than another method. In this work we propose three such metrics to evaluate BDL models designed specifically for the task of semantic segmentation. We modify DeepLab-v3+, one of the state-of-the-art deep neural networks, and create its Bayesian counterpart using MC dropout and Concrete dropout as inference techniques. We then compare and test these two inference techniques on the well-known Cityscapes dataset using our suggested metrics. Our results provide new benchmarks for researchers to compare and evaluate their improved uncertainty quantification in pursuit of safer semantic segmentation.

Connected through hyperedge HE-1 (induced by source paper "scalable uncertainty for computer vision with functional variational inference"):
  with members:
    - "deepfactors real time probabilistic dense monocular slam"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "functional variational bayesian neural networks"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "efficient uncertainty estimation for semantic segmentation in videos"

Connected through hyperedge HE-2 (induced by source paper "learnable bernoulli dropout for bayesian deep learning"):
  with members:
    - "adaptive activity monitoring with uncertainty quantification in switching gaussian process models"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "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-3 (induced by source paper "monoloco monocular 3d pedestrian localization and uncertainty estimation"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pifpaf composite fields for human pose estimation"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-4 (induced by source paper "evaluating scalable bayesian deep learning methods for robust computer vision"):
  with members:
    - "evaluating model calibration in classification"
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "lightweight probabilistic deep networks"
    - "ocnet object context network for scene parsing"
    - "pointpillars fast encoders for object detection from point clouds"
    - "pyro deep universal probabilistic programming"

Connected through hyperedge HE-5 (induced by source paper "the fishyscapes benchmark measuring blind spots in semantic segmentation"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "fusion volumetric object level slam"
    - "semantic foggy scene understanding with synthetic data"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "a simple baseline for bayesian uncertainty in deep learning"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "swalp stochastic weight averaging in low precision training"
    - "subspace inference for bayesian deep learning"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "averaging weights leads to wider optima and better generalization"
    - "gradient descent happens in a tiny subspace"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"

Connected through hyperedge HE-7 (induced by source paper "uncertainty aware audiovisual activity recognition using deep bayesian variational inference"):
  with members:
    - "deep audio visual speech recognition"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "moments in time dataset one million videos for event understanding"
    - "learning to separate object sounds by watching unlabeled video"
    - "audio visual scene analysis with self supervised multisensory features"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "what actions are needed for understanding human actions in videos"

Connected through hyperedge HE-8 (induced by source paper "tradi tracking deep neural network weight distributions"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "lca loss change allocation for neural network training"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "ionet learning to cure the curse of drift in inertial odometry"

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)