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: "an empirical study into annotator agreement ground truth estimation and algorithm evaluation"
  Abstract: Although agreement between the annotators who mark feature locations within images has been studied in the past from a statistical viewpoint, little work has attempted to quantify the extent to which this phenomenon affects the evaluation of foreground–background segmentation algorithms. Many researchers utilize ground truth (GT) in experimentation and more often than not this GT is derived from one annotator’s opinion. How does the difference in opinion affects an algorithm’s evaluation? A methodology is applied to four image-processing problems to quantify the interannotator variance and to offer insight into the mechanisms behind agreement and the use of GT. It is found that when detecting linear structures, annotator agreement is very low. The agreement in a structure’s position can be partially explained through basic image properties. Automatic segmentation algorithms are compared with annotator agreement and it is found that there is a clear relation between the two. Several GT estimation methods are used to infer a number of algorithm performances. It is found that the rank of a detector is highly dependent upon the method used to form the GT, and that although STAPLE and LSML appear to represent the mean of the performance measured using individual annotations, when there are few annotations, or there is a large variance in them, these estimates tend to degrade. Furthermore, one of the most commonly adopted combination methods—consensus voting—accentuates more obvious features, resulting in an overestimation of performance. It is concluded that in some data sets, it is not possible to confidently infer an algorithm ranking when evaluating upon one GT.

Connected through hyperedge HE-1 (induced by source paper "segmentation of roots in soil with u net"):
  with members:
    - "squeeze excite guided few shot segmentation of volumetric images"
    - "flow based self supervised pixel embedding for image segmentation"
    - "on compressing u net using knowledge distillation"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "cnn based segmentation of medical imaging data"

Connected through hyperedge HE-2 (induced by source paper "adaloss adaptive loss function for landmark localization"):
  with members:
    - "style aggregated network for facial landmark detection"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "pose invariant face alignment with a single cnn"
    - "improving landmark localization with semi supervised learning"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "stacked hourglass networks for human pose estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "handling inter annotator agreement for automated skin lesion segmentation"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "data augmentation for skin lesion analysis"
    - "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018"
    - "knowledge transfer for melanoma screening with deep learning"
    - "towards automated melanoma screening proper computer vision reliable results"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"

Connected through hyperedge HE-4 (induced by source paper "supervised uncertainty quantification for segmentation with multiple annotations"):
  with members:
    - "highly accurate model for prediction of lung nodule malignancy with ct scans"
    - "uncertainty driven sanity check application to postoperative brain tumor cavity segmentation"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-5 (induced by source paper "assessing data quality of annotations with krippendorff alpha for applications in computer vision"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "focal loss for dense object detection"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "microsoft coco common objects in context"
    - "identifying mislabeled training data"

Connected through hyperedge HE-6 (induced by source paper "why rankings of biomedical image analysis competitions should be interpreted with care"):
  with members:
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "imagenet large scale visual recognition challenge"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-7 (induced by source paper "discovery radiomics for pathologically proven computed tomography lung cancer prediction"):
  with members:
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "anatomy specific classification of medical images using deep convolutional nets"
    - "deep convolutional networks for pancreas segmentation in ct imaging"
    - "matconvnet convolutional neural networks for matlab"

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)