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 in multitask learning joint representations for probabilistic mr only radiotherapy planning"
  Abstract: Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task network that estimates: (1) intrinsic uncertainty through a heteroscedastic noise model for spatially-adaptive task loss weighting and (2) parameter uncertainty through approximate Bayesian inference. This allows sampling of multiple segmentations and synCTs that share their network representation. We test our model on prostate cancer scans and show that it produces more accurate and consistent synCTs with a better estimation in the variance of the errors, state of the art results in OAR segmentation and a methodology for quality assurance in radiotherapy treatment planning.

Connected through hyperedge HE-1 (induced by source paper "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "taskonomy disentangling task transfer learning"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "niftynet a deep learning platform for medical imaging"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "age progression regression by conditional adversarial autoencoder"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-2 (induced by source paper "multi task recurrent convolutional network with correlation loss for surgical video analysis"):
  with members:
    - "incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "deepphase surgical phase recognition in cataracts videos"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"

Connected through hyperedge HE-3 (induced by source paper "qanet quality assurance network for image segmentation"):
  with members:
    - "yolov3 an incremental improvement"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "microscopy cell segmentation via convolutional lstm networks"
    - "microscopy cell segmentation via adversarial neural networks"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"):
  with members:
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"
    - "propagating uncertainty in multi stage bayesian convolutional neural networks with application to pulmonary nodule detection"
    - "team delft s robot winner of the amazon picking challenge 2016"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "imagenet large scale visual recognition challenge"

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)