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: "what uncertainties do we need in bayesian deep learning for computer vision"
  Abstract: There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling epistemic vs. aleatoric uncertainty in Bayesian deep learning models for vision tasks. For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty. We study models under the framework with per-pixel semantic segmentation and depth regression tasks. Further, our explicit uncertainty formulation leads to new loss functions for these tasks, which can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.

Connected through hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "model based reinforcement learning for atari"
    - "adversarial video generation on complex datasets"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

Connected through hyperedge HE-2 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "panoptic segmentation"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "visual odometry revisited what should be learnt"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"

Connected through hyperedge HE-3 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "asking easy questions a user friendly approach to active reward learning"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "functional variational bayesian neural networks"
    - "causal confusion in imitation learning"
    - "a divergence minimization perspective on imitation learning methods"
    - "a simple baseline for bayesian uncertainty in deep learning"

Connected through hyperedge HE-4 (induced by source paper "panda a gigapixel level human centric video dataset"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "widerperson a diverse dataset for dense pedestrian detection in the wild"
    - "group reidentification with multigrained matching and integration"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "vision meets drones a challenge"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"

Connected through hyperedge HE-5 (induced by source paper "an empirical evaluation on robustness and uncertainty of regularization methods"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "dropblock a regularization method for convolutional networks"
    - "feature denoising for improving adversarial robustness"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial logit pairing"
    - "autoaugment learning augmentation policies from data"
    - "shakedrop regularization for deep residual learning"

Connected through hyperedge HE-6 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "disentangling monocular 3d object detection"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "monocular 3d object detection via geometric reasoning on keypoints"
    - "fcos fully convolutional one stage object detection"
    - "m3d rpn monocular 3d region proposal network for object detection"

Connected through hyperedge HE-7 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving"):
  with members:
    - "deep evidential regression"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "learning multimodal fixed point weights using gradient descent"
    - "pixor real time 3d object detection from point clouds"
    - "deep active learning for efficient training of a lidar 3d object detector"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-8 (induced by source paper "calibration of pre trained transformers"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "calibration of encoder decoder models for neural machine translation"
    - "simplified neural unsupervised domain adaptation"
    - "evaluating lottery tickets under distributional shifts"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "revealing the dark secrets of bert"
    - "how to fine tune bert for text classification"

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)