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 "derivative manipulation for general example weighting"):
  Abstract: We propose derivative manipulation (DM) for training accurate and robust softmax-based deep neural networks, for two reasons: (1) In gradient-based optimisation, manipulating the derivative directly is more straightforward than designing loss functions, and it has a direct impact on the update of a model. (2) A loss function's derivative magnitude function can be understood as a weighting scheme; the loss's derivative of an example defines how much impact it has on the update of a model. Therefore, manipulating the derivative is to adjust the weighting scheme. DM simply modifies the derivative magnitude, including transformation and normalisation, after which the derivative magnitude function is termed emphasis density function (EDF). An EDF is a formula expressing an example weighting scheme and we may deduce many options for EDFs from common probability density functions (PDFs). We demonstrate the effectiveness of the DM formulation empirically by extensive experiments on both vision and language tasks, especially when adverse conditions exist, e.g., noisy data and sample imbalance.

Includes members:
  Member "symmetric cross entropy for robust learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning from web data with self organizing memory module")
  Member "nlnl negative learning for noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning from web data with self organizing memory module")
  Member "deep learning is robust to massive label noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative pseudo label refinement for unsupervised domain adaptation")
  Member "joint optimization framework for learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "dimensionality driven learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "deep metric learning by online soft mining and class aware attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "instance cross entropy for deep metric learning")
  Member "learning to reweight examples for robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "generalized cross entropy loss for training deep neural networks with noisy labels":
    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.LG(Machine Learning)