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 "improving generalization and robustness with noisy collaboration in knowledge distillation"):
  Abstract: Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise at different levels in a knowledge distillation framework. We introduce "Fickle Teacher" which provides variable supervision signals to the student for the same input. We observe that the response variability from the teacher results in a significant generalization improvement in the student. We further propose "Soft-Randomization" as a novel technique for improving robustness to input variability in the student. This minimizes the dissimilarity between the student's distribution on noisy data with teacher's distribution on clean data. We show that soft-randomization, even with low noise intensity, improves the robustness significantly with minimal drop in generalization. Lastly, we propose a new technique, "Messy-collaboration", which introduces target variability, whereby student and/or teacher are trained with randomly corrupted labels. We find that supervision from a corrupted teacher improves the adversarial robustness of student significantly while preserving its generalization and natural robustness. Our extensive empirical results verify the effectiveness of adding constructive noise in the knowledge distillation framework for improving the generalization and robustness of the model.

Includes members:
  Member "using videos to evaluate image model robustness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "deep self learning from noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "symmetric cross entropy for robust learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning from web data with self organizing memory module")
  Member "simple and effective regularization methods for training on noisily labeled data with generalization guarantee":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "identifying mislabeled data using the area under the margin ranking")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "when does label smoothing help":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "natural adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "on the sensitivity of adversarial robustness to input data distributions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "g odel s sentence is an adversarial example but unsolvable")

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)