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 "parabolic approximation line search an efficient and effective line search approach for dnns"):
  Abstract: This work shows that line searches performed only over the loss function defined by the current batch can successfully compete with common optimization methods on state-of-the-art architectures in wall clock time. In other words, our approach is performing competitively, despite for the most part ignoring the noise originating from batch sampling of the loss function. Furthermore, we empirically show that local minima on lines in direction of the negative gradient can be estimated almost perfectly by a parabolic approximation. This suggests that the loss function is at least locally convex, which mitigates the common perception of a highly non convex loss landscape. Our approach combines well-known methods such as parabolic approximation, line search and conjugate gradient, to perform an efficient line search. To evaluate general performance as well as the hyper parameter sensitivity of our optimizer, we performed multiple comprehensive hyperparameter grid searches on several datasets and architectures. In addition, we provide a convergence prove on a simplified scenario. PyTorch and Tensorflow implementations are provided at https://github.com/cogsys-tuebingen/PAL.

Includes members:
  Member "adaptive gradient methods with dynamic bound of learning rate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a metric learning reality check")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "efficientnet rethinking model scaling for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "l4 practical loss based stepsize adaptation for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "collaborative sampling in generative adversarial networks")
  Member "neither quick nor proper evaluation of quickprop for learning deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pal a fast dnn optimization method based on curvature information")
  Member "probabilistic line searches for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "painless stochastic gradient interpolation line search and convergence rates")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)