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 deep image classification"
  Abstract: Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human effort. In this paper, we propose a novel active learning (AL) framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner. Our approach advances the existing AL methods in two aspects. First, we incorporate deep convolutional neural networks into AL. Through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. Second, we present a cost-effective sample selection strategy to improve the classification performance with less manual annotations. Unlike traditional methods focusing on only the uncertain samples of low prediction confidence, we especially discover the large amount of high-confidence samples from the unlabeled set for feature learning. Specifically, these high-confidence samples are automatically selected and iteratively assigned pseudolabels. We thus call our framework cost-effective AL (CEAL) standing for the two advantages. Extensive experiments demonstrate that the proposed CEAL framework can achieve promising results on two challenging image classification data sets, i.e., face recognition on the cross-age celebrity face recognition data set database and object categorization on Caltech-256.

Connected through hyperedge HE-1 (induced by source paper "active and incremental learning with weak supervision"):
  with members:
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "yolov3 an incremental improvement"
    - "active learning for deep object detection"
    - "focal loss for dense object detection"
    - "extreme clicking for efficient object annotation"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-2 (induced by source paper "reinforced active learning for image segmentation"):
  with members:
    - "multi target embodied question answering"
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "panoptic feature pyramid networks"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "learning a policy for opportunistic active learning"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "discovering general purpose active learning strategies"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"

Connected through hyperedge HE-3 (induced by source paper "curriculum labeling self paced pseudo labeling for semi supervised learning"):
  with members:
    - "on the power of curriculum learning in training deep networks"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "averaging weights leads to wider optima and better generalization"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "active learning based classification in automated connected vehicles"):
  with members:
    - "ultra reliable and low latency vehicular communication an active learning approach"
    - "active self paced learning for cost effective and progressive face identification"
    - "learning with symmetric label noise the importance of being unhinged"

Connected through hyperedge HE-5 (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-6 (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 melanoma segmentation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-7 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "continual lifelong learning with neural networks a review"
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"

Connected through hyperedge HE-8 (induced by source paper "informative sample generation using class aware generative adversarial networks for classification of chest xrays"):
  with members:
    - "training data independent image registration with gans using transfer learning and segmentation information"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "gans for biological image synthesis"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "end to end unsupervised deformable image registration with a convolutional neural network"

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)