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: "suggestive annotation a deep active learning framework for biomedical image segmentation"
  Abstract: Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. However, due to large variations in biomedical images (different modalities, image settings, objects, noise, etc), to utilize deep learning on a new application, it usually needs a new set of training data. This can incur a great deal of annotation effort and cost, because only biomedical experts can annotate effectively, and often there are too many instances in images (e.g., cells) to annotate. In this paper, we aim to address the following question: With limited effort (e.g., time) for annotation, what instances should be annotated in order to attain the best performance? We present a deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas. We utilize uncertainty and similarity information provided by FCN and formulate a generalized version of the maximum set cover problem to determine the most representative and uncertain areas for annotation. Extensive experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node ultrasound image segmentation dataset show that, using annotation suggestions by our method, state-of-the-art segmentation performance can be achieved by using only 50% of training data.

Connected through hyperedge HE-1 (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-2 (induced by source paper "task aware variational adversarial active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "variational adversarial active learning"
    - "learning loss for active learning"
    - "ranking cgans subjective control over semantic image attributes"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deep bayesian active learning with image data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "dlbc a deep learning based consensus in blockchains for deep learning services"):
  with members:
    - "energy recycling blockchain with proof of deep learning"
    - "exploiting computation power of blockchain for biomedical image segmentation"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"
    - "proof of search combining blockchain consensus formation with solving optimization problems"
    - "scnn a general distribution based statistical convolutional neural network with application to video object detection"
    - "privacy aware cost effective scheduling considering non schedulable appliances in smart home"
    - "real time adversarial attacks"
    - "measuring human perception to improve handwritten document transcription"

Connected through hyperedge HE-4 (induced by source paper "active learning for entity alignment"):
  with members:
    - "active learning for graph neural networks via node feature propagation"
    - "knowledge graph entity alignment with graph convolutional networks lessons learned"
    - "knowledge graph alignment network with gated multi hop neighborhood aggregation"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "lasagne locality and structure aware graph node embedding"
    - "active learning for graph embedding"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"

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"
    - "cost effective active learning for deep image classification"
    - "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 "exploiting unlabeled data in cnns by self supervised learning to rank"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "on the use of deep learning for blind image quality assessment"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

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)