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: "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"
  Abstract: In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriate for modeling complex and noisy human behaviors. The proposed uncertainty acquisition can be done with a single forward path without Monte Carlo sampling and is suitable for real-time robotics applications. The properties of the proposed uncertainty measure are analyzed through three different synthetic examples, absence of data, heavy measurement noise, and composition of functions scenarios. We show that each case can be distinguished using the proposed uncertainty measure and presented an uncertainty-aware learn- ing from demonstration method of an autonomous driving using this property. The proposed uncertainty-aware learning from demonstration method outperforms other compared methods in terms of safety using a complex real-world driving dataset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "learning what you can do before doing anything"
    - "flow grounded spatial temporal video prediction from still images"
    - "action anticipation by predicting future dynamic images"

Connected through hyperedge HE-3 (induced by source paper "learning resilient behaviors for navigation under uncertainty environments"):
  with members:
    - "a general framework for uncertainty estimation in deep learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "learning navigation behaviors end to end with autorl"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to drive in a day"

Connected through hyperedge HE-4 (induced by source paper "dave i can assure you that it s going to be all right a definition case for and survey of algorithmic assurances in human autonomy trust relationships"):
  with members:
    - "expressing robot incapability"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "enabling robots to communicate their objectives"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "challenges for transparency"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "inverse reward design"

Connected through hyperedge HE-5 (induced by source paper "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"):
  with members:
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "bounding box regression with uncertainty for accurate object detection"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "receptive field block net for accurate and fast object detection"
    - "single shot refinement neural network for object detection"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-6 (induced by source paper "visual based autonomous driving deployment from a stochastic and uncertainty aware perspective"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "real to virtual domain unification for end to end autonomous driving"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "multimodal unsupervised image to image translation"
    - "safe reinforcement learning with model uncertainty estimates"
    - "drit diverse image to image translation via disentangled representations"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "learning an uncertainty aware object detector for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "acquisition of localization confidence for accurate object detection"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "birdnet a 3d object detection framework from lidar information"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-8 (induced by source paper "evaluating scalable uncertainty estimation methods for dnn based molecular property prediction"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "evaluating and calibrating uncertainty prediction in regression tasks"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "bayesian semi supervised learning for uncertainty calibrated prediction of molecular properties and active learning"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "on calibration of modern neural networks"

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)