human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning to segment skin lesions from noisy annotations"):
  Abstract: Deep convolutional neural networks have driven substantial advancements in the automatic understanding of images. Requiring a large collection of images and their associated annotations is one of the main bottlenecks limiting the adoption of deep networks. In the task of medical image segmentation, requiring pixel-level semantic annotations performed by human experts exacerbate this difficulty. This paper proposes a new framework to train a fully convolutional segmentation network from a large set of cheap unreliable annotations and a small set of expert-level clean annotations. We propose a spatially adaptive reweighting approach to treat clean and noisy pixel-level annotations commensurately in the loss function. We deploy a meta-learning approach to assign higher importance to pixels whose loss gradient direction is closer to those of clean data. Our experiments on training the network using segmentation ground truth corrupted with different levels of annotation noise show how spatial reweighting improves the robustness of deep networks to noisy annotations.

Includes members:
  Member "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learned enrichment of top view grid maps improves object detection")
  Member "star shape prior in fully convolutional networks for skin lesion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation")
  Member "attention u net learning where to look for the pancreas":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network")
  Member "learning to reweight examples for robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "iterative learning with open set noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "mentornet learning data driven curriculum for very deep neural networks on corrupted labels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "toward robustness against label noise in training deep discriminative neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "learning from noisy large scale datasets with minimal supervision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")

Question: Please tell me which class the source paper 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)