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: "robust learning at noisy labeled medical images applied to skin lesion classification"
  Abstract: Deep neural networks (DNNs) have achieved great success in a wide variety of medical image analysis tasks. However, these achievements indispensably rely on the accurately-annotated datasets. If with the noisy-labeled images, the training procedure will immediately encounter difficulties, leading to a suboptimal classifier. This problem is even more crucial in the medical field, given that the annotation quality requires great expertise. In this paper, we propose an effective iterative learning framework for noisy-labeled medical image classification, to combat the lacking of high quality annotated medical data. Specifically, an online uncertainty sample mining method is proposed to eliminate the disturbance from noisy-labeled images. Next, we design a sample re-weighting strategy to preserve the usefulness of correctly-labeled hard samples. Our proposed method is validated on skin lesion classification task, and achieved very promising results.

Connected through hyperedge HE-1 (induced by source paper "data valuation using reinforcement learning"):
  with members:
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "learning to transfer learn"
    - "uncertainty based detection and relabeling of noisy image labels"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "an empirical model of large batch training"
    - "choicenet robust learning by revealing output correlations"
    - "domain adaptive transfer learning with specialist models"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-2 (induced by source paper "cia net robust nuclei instance segmentation with contour aware information aggregation"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "gans for medical image analysis"
    - "path aggregation network for instance segmentation"
    - "decoupled weight decay regularization"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "training deep neural networks on noisy labels with bootstrapping"

Connected through hyperedge HE-3 (induced by source paper "pick and learn automatic quality evaluation for noisy labeled image segmentation"):
  with members:
    - "joint optimization framework for learning with noisy labels"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "learning from noisy large scale datasets with minimal supervision"
    - "understanding deep learning requires rethinking generalization"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "an active learning approach for reducing annotation cost in skin lesion analysis"):
  with members:
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "mixup beyond empirical risk minimization"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "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"

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)