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: "cost effective active learning for melanoma segmentation"
  Abstract: We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance. The source code of this project is available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "angle closure detection in anterior segment oct based on multilevel deep network"
    - "chestnet a deep neural network for classification of thoracic diseases on chest radiography"
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"

Connected through hyperedge HE-2 (induced by source paper "viewal active learning with viewpoint entropy for semantic segmentation"):
  with members:
    - "learning loss for active learning"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cost effective active learning for deep image classification"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-3 (induced by source paper "deep active learning for efficient training of a lidar 3d object detector"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "localization aware active learning for object detection"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"

Connected through hyperedge HE-4 (induced by source paper "variational adversarial active learning"):
  with members:
    - "uncertainty guided continual learning with bayesian neural networks"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "cost sensitive active learning for intracranial hemorrhage detection"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "adversarial sampling for active learning"
    - "gradient free policy architecture search and adaptation"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "o medal online active deep learning for medical image analysis"):
  with members:
    - "bach grand challenge on breast cancer histology images"
    - "a survey on deep learning in medical image analysis"
    - "deep bayesian active learning with image data"
    - "understanding deep learning requires rethinking generalization"
    - "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"
    - "rethinking the inception architecture for computer vision"
    - "efficient estimation of word representations in vector space"

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)