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 from large scale noisy web data with ubiquitous reweighting for image classification"):
  Abstract: Many advances of deep learning techniques originate from the efforts of addressing the image classification task on large-scale datasets. However, the construction of such clean datasets is costly and time-consuming since the Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In this paper, we propose a Ubiquitous Reweighting Network (URNet) that learns an image classification model from large-scale noisy data. By observing the web data, we find that there are five key challenges, \ie, imbalanced class sizes, high intra-classes diversity and inter-class similarity, imprecise instances, insufficient representative instances, and ambiguous class labels. To alleviate these challenges, we assume that every training instance has the potential to contribute positively by alleviating the data bias and noise via reweighting the influence of each instance according to different class sizes, large instance clusters, its confidence, small instance bags and the labels. In this manner, the influence of bias and noise in the web data can be gradually alleviated, leading to the steadily improving performance of URNet. Experimental results in the WebVision 2018 challenge with 16 million noisy training images from 5000 classes show that our approach outperforms state-of-the-art models and ranks the first place in the image classification task.

Includes members:
  Member "joint optimization framework for learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "learning from noisy web data with category level supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi class classification from noisy similarity labeled data")
  Member "iterative learning with open set noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "dual path networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional networks with dense connectivity")
  Member "a survey on deep learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "medgan medical image translation using gans")
  Member "webvision database visual learning and understanding from web data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  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)