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 estimates and multi hypotheses networks for optical flow"
  Abstract: Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology. In this paper, we make such networks estimate their local uncertainty about the correctness of their prediction, which is vital information when building decisions on top of the estimations. For the first time we compare several strategies and techniques to estimate uncertainty in a large-scale computer vision task like optical flow estimation. Moreover, we introduce a new network architecture utilizing the Winner-Takes-All loss and show that this can provide complementary hypotheses and uncertainty estimates efficiently with a single forward pass and without the need for sampling or ensembles. Finally, we demonstrate the quality of the different uncertainty estimates, which is clearly above previous confidence measures on optical flow and allows for interactive frame rates.

Connected through hyperedge HE-1 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost"):
  with members:
    - "adaptative inference cost with convolutional neural mixture models"
    - "depth adaptive transformer"
    - "hydra an ensemble of convolutional neural networks for geospatial land classification"
    - "robust training with ensemble consensus"
    - "deep tree learning for zero shot face anti spoofing"
    - "improved techniques for training adaptive deep networks"
    - "searching for mobilenetv3"
    - "universally slimmable networks and improved training techniques"

Connected through hyperedge HE-2 (induced by source paper "adaptative inference cost with convolutional neural mixture models"):
  with members:
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "maskconnect connectivity learning by gradient descent"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-3 (induced by source paper "learning deep conditional target densities for accurate regression"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "deep high resolution representation learning for human pose estimation"
    - "bottom up object detection by grouping extreme and center points"
    - "lightweight probabilistic deep networks"
    - "cornernet detecting objects as paired keypoints"
    - "atom accurate tracking by overlap maximization"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "bounding box regression with uncertainty for accurate object detection"

Connected through hyperedge HE-4 (induced by source paper "exploring uncertainty in conditional multi modal retrieval systems"):
  with members:
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "features for multi target multi camera tracking and re identification"
    - "two stream self supervised learning for action recognition"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-5 (induced by source paper "mono sf multi view geometry meets single view depth for monocular scene flow estimation of dynamic traffic scenes"):
  with members:
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "deep rigid instance scene flow"
    - "lightweight probabilistic deep networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "freihand a dataset for markerless capture of hand pose and shape from single rgb images"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "learning joint reconstruction of hands and manipulated objects"
    - "3d hand shape and pose from images in the wild"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "large scale multiview 3d hand pose dataset"
    - "learning to estimate 3d hand pose from single rgb images"
    - "learning a multi view stereo machine"

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)