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 "trust your model iterative label improvement and robust training by confidence based filtering and dataset partitioning"):
  Abstract: State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to label errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of iterative training and prediction steps, both label quality and resulting model accuracy can be improved significantly. Our method achieves state-of-the-art results, while being architecture agnostic and therefore broadly applicable. Compared to other methods dealing with erroneous labels, our approach does neither require another network to be trained, nor does it necessarily need an additional, highly accurate reference label set. Instead of removing samples from a labelled set, our technique uses additional sensor data without the need for manual labelling.

Includes members:
  Member "deep semantic segmentation of natural and medical images a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks")
  Member "self learning to filter noisy labels with self ensembling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identifying mislabeled data using the area under the margin ranking")
  Member "training object detectors with noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards noise resistant object detection with noisy annotations")
  Member "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "robust learning under label noise with iterative noise filtering":
    also connected through other hyperedges:
      (none)
  Member "an overview of deep learning in medical imaging focusing on mri":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic detection and counting of retina cell nuclei using deep learning")
  Member "learning to reweight examples for robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "mentornet learning data driven curriculum for very deep neural networks on corrupted labels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")

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.LG(Machine Learning)